Machine Learning Engineer

• Architect and integrate hybrid AI systems that combine traditional machine learning, deep learning, large language models (LLMs), and retrieval-augmented generation (RAG) pipelines.
• Design and deploy scalable AI architectures including APIs, microservices, and model-serving frameworks that integrate seamlessly with analytic, simulation, or operational systems.
• Lead the full AI/ML lifecycle — from data ingestion and feature engineering through training, deployment, and sustainment within secure DoD environments (IL5/IL6, ATO, GovCloud).
• Engineer event-driven data pipelines and feature stores for both structured and unstructured data, including text, imagery, and simulation outputs.
• Ensure Responsible AI practices by embedding traceability, explainability, and confidence scoring into deployed systems.
• Implement and maintain MLOps pipelines (MLflow, Kubeflow, Airflow, Docker/Kubernetes) to support continuous integration, retraining, and drift detection.
• Transition R&D prototypes into production, optimizing for mission constraints such as limited compute, edge environments, or disconnected operations.
• Provide technical leadership and mentorship, setting standards for model quality, architectural design, and ethical AI deployment across programs.
• Collaborate across engineering, data, and modeling teams to unify FTI’s AI portfolio, ensuring interoperability and reuse across mission systems.
• Support proposal and solution development, providing technical inputs for AI/ML architectures, data strategies, and Responsible AI assurance frameworks.

Lead AI/ML Engineer

• Design, develop, and deploy AI/ML models and pipelines that meet mission and performance objectives.
• Build, train, and fine-tune models using frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, and LangChain.
• Develop and operationalize MLOps pipelines (MLflow, Kubeflow, DVC, or custom training/inference orchestration).
• Implement and optimize vector databases (Milvus, Pinecone, Chroma, FAISS) and retrieval architectures (RAG, graph, hybrid).
• Write clean, efficient Python code for data ingestion, feature engineering, embeddings, and inference services.
• Experiment with fine-tuning and optimization of LLMs and task-specific models (LoRA, QLoRA, PEFT).
• Contribute to agent-based applications using frameworks like LangGraph, AutoGen, CrewAI, or DSPy.
• Integrate AI services into real-world systems via APIs, event-driven workflows, or UI copilots.
• Collaborate with data engineers, software developers, and mission analysts to ensure AI models are production-ready and aligned with customer needs.
• Participate in peer reviews, contribute to shared repositories, and document models and experiments for reproducibility.

AI/ML Engineer

AI Research Scientist

• Engage in relevant research and development (R&D) of AI systems, models, and advanced machine learning algorithms that augment physics-driven modeling and simulation systems
• Explore and implement AI-powered simulation tooling in support of AI workflows through reinforcement learning, multi-agent systems, and hybrid modeling approaches
• Collaborate with research, engineering, and product teams to build AI-powered solutions that meet mission-critical modeling and decision-support needs
• Engage in and support the drafting and review of conference and journal articles and presentations, sharing advances with both internal stakeholders and the wider research community.
• Contribute content to technical invention disclosures, including associated narrative, graphics, and engagements in support of patent development
• Perform additional responsibilities (no more than 10% of duties) in support of the company’s technology and product development initiatives.

AI Research Scientist, Applied AI

LLM Engineer

• Own and drive the global 4P strategy for ACS AI infrastructure platforms, from annual planning through execution and performance tracking.
• Define clear value propositions, positioning, and segmentation for AI infrastructure solutions aligned to customer workloads and industry use cases.
• Drive execution of the AI GTM plan with regions and countries, ensuring timely rollout and consistency.
• Act as the WW GTM expert for ACS AI infrastructure solutions (e.g., ZGX Nano, ZGX Fury, Boost), ensuring tight alignment between product capabilities and market needs.
• Translate complex AI infrastructure architectures into compelling, sales‑ready GTM narratives and offers.
• Partner with GBU and engineering teams to influence roadmap priorities based on market and customer feedback.
• Support priority deals, lighthouse customers, and strategic pursuits requiring AI infrastructure expertise.
• Enable sales teams with clear guidance, playbooks, and tools to accelerate AI‑led pipeline generation and conversion.
• Collaborate closely with alliance teams (e.g., NVIDIA, AMD, Intel, ISVs) to deliver joint GTM programs, aligned launches, and co‑selling motions tied to AI infrastructure platforms.
• Define and track KPIs to measure GTM effectiveness, pipeline impact, and revenue outcomes.

Global GTM & 4P Category Manager – AI Infrastructure

• Leading a small team consisting of developers and researchers to implement AI/ML algorithms to solve computer vision problems in remote sensing
• Analyzing and pre-processing large remote sensing datasets
• Researching supervised and unsupervised ATR solutions for remote sensing
• Researching the performance benefits of multimodal data fusion for ATR, under diverse data collection scenarios
• Training, optimizing, and deploying deep learning CV models that provide analysts with actionable insights
• Reviewing relevant publications to understand cutting edge concepts, break down key ideas to government customers, and apply to defense and commercial applications
• Writing technical documentation supporting code, program capabilities and user-guides

Staff Computer Vision AI/ML Engineer

• We're moving from a product where AI is a feature you can turn on to one where it's a layer that runs through everything: response suggestions, abuse detection, summarization, lead scoring, intent classification.
• You own the ML and AI engineering layer end to end.
• Model registry, feature pipelines, and deployment pathways that any engineer in the org can use.
• Evaluation infrastructure that catches regressions before they hit prod, not after.
• Drift detection, online evals, cost and latency monitoring.
• Rollback and progressive rollout patterns built for ML systems, not retrofitted from generic CD.
• Build-vs-buy calls. Know when a frontier API is the right answer, when a managed service is fine, when to fine-tune, and when a regex would have done the job.

Senior ML Engineer

AI Engineer

• Design, build, and deploy AI-powered tools and automations for HR and Talent use cases, including intelligent workflow automations, employee-facing chatbots, document processing solutions, and decision-support systems
• Identify opportunities to improve manual or repetitive HR workflows through scalable AI/ML-driven automation solutions
• Develop integrations between AI solutions and enterprise systems including Workday, ATS platforms, ServiceNow Ticketing, and other internal tools ensuring seamless data flow and user experience
• Partner directly with HR Operations and cross-functional stakeholders to gather requirements, prototype solutions, run pilots, and drive successful adoption
• Continuously monitor and improve deployed tools based on user feedback, operational metrics, and evolving business needs
• Create technical documentation, implementation guides, and operational handoff materials to support long-term maintainability and scalability.

Principal AI Engineer, HR Automation

• Design, develop, and deploy AI, machine learning, and generative AI solutions
• Partner with the CIO and stakeholders to identify, prioritize, and scope AI/ML/GenAI use cases
• Prototype and deliver proofs-of-concept for emerging AI technologies
• Translate validated proofs-of-concept into clear functional and non-functional requirements
• Build and maintain semantic/domain models
• Develop AI-enabled applications and demos using Streamlit and APIs
• Implement solutions that leverage AWS services
• Collaborate with BI, Architecture, and Security teams
• Develop and implement automation solutions for the QA department
• Create and maintain detailed documentation for AI models, processes, and workflows

Senior AI Developer – Engineer

• Architect the ranking backbone for query understanding and personalization
• Design and build a search autosuggest system
• Develop production-grade Multi-Task Learning
• Own the inference layer for quality optimization
• Advance evaluation practices for long-horizon metrics
• Partner across teams to translate business goals into ranking policies
• Mentor ML engineers in ranking expertise

Senior Machine Learning Engineer II, Search & Recommendations Ranking

• Develop and maintain machine learning models that are integral to our production decision-making system and directly influencing business outcomes.
• Adopt or build new technologies for training and serving ML models (e.g. support large models, increase developer velocity, etc)
• Monitor the latest ML research for functional ideas that the team could try.
• Optimize ML Pipelines – Build and scale efficient pipelines for real-time and batch processing.
• Model Monitoring & Improvement – Track performance, detect drift, and automate retraining.
• Use strong communication skills (verbal and written) to explain statistical and machine learning concepts to both technical and non-technical audiences
• Collaborate with a team of world-class engineers with diverse backgrounds.
• Be part of an “engineering excellence” culture through state-of-the-art tools, risk-driven testing, explainable systems, and code review.

Machine Learning Engineer, Conversion ML

• Design and deploy end-to-end agentic AI workflows using planner–worker, orchestrator–executor, multi-agent, and RAG-based architectures
• Build reusable components and reference patterns for tool routing, state management, error handling, and human-in-the-loop checkpoints
• Implement robust retrieval pipelines (hybrid search, vector + keyword, graph-aware retrieval) over technical documents, historian data, and operational records
• Translate domain problems from drilling, completions, production, geophysics, and power plant operations into well-scoped agentic use cases with clear success metrics
• Build and maintain a semantic layer over our data lake and warehouse using Snowflake Semantic Views and Databricks Unity Catalog Metric Views, making business concepts queryable by both humans and agents
• Develop and curate knowledge graphs that connect domain entities (wells, pads, assets, equipment, events, documents) and serve as grounding context for LLM reasoning
• Standardize how agents access enterprise data through Model Context Protocol (MCP) servers and equivalent integration patterns
• Establish agent evaluation frameworks including golden datasets, automated regression tests, and structured evals for accuracy, faithfulness, and tool-use correctness
• Implement tracing, logging, and observability across agent runs to support debugging, cost monitoring, and continuous improvement
• Build feedback loops that capture user input and convert it into eval cases and prompt/system improvements
• Support production incidents and platform-level issues impacting deployed agents
• Deploy agents as production services on our Azure-native stack (App Service, Container Apps, Functions) with Entra ID SSO, Key Vault-managed secrets, and proper cost controls
• Build lightweight UIs (Streamlit, Gradio, or React) for agentic applications and internal tools
• Lead design reviews and cross-functional enablement sessions on agentic AI patterns and best practices

• Build and maintain backend services that support agent execution, orchestration, and lifecycle management.
• Develop systems for workflow coordination, state management, and tool integration.
• Write high-quality, maintainable code in Python to power platform capabilities and APIs.
• Deploy and operate services on Kubernetes, ensuring reliability and scalability.
• Contribute to systems for observability, logging, tracing, and debugging of distributed workflows.
• Improve system performance across latency, throughput, and fault tolerance.
• Build internal tools and APIs that improve the developer experience for teams using the platform.
• Collaborate with engineers, product managers, and AI teams to deliver production-ready solutions.
• Participate in design discussions and contribute to system architecture and technical decisions.

Software Engineer, Senior Software Engineer – AI Platform

• Partner with the AI Process Lead and engineering teams to execute prioritized AI productivity initiatives
• Embed with teams to understand day-to-day coding workflows and identify practical improvement opportunities
• Create and maintain team-level AI artifacts, including AGENTS.md, .github/copilot-instructions.md, reusable prompts, and workflow playbooks
• Build and refine AI personas for concrete use cases (for example: PR reviewer, test gap detector, refactoring assistant)
• Implement AI-assisted code review and developer workflow automations (GitHub, CI/CD, scripts, internal tooling)
• Prototype and deliver tools in Python (or similar), with clear documentation and adoption guidance
• Track and report impact metrics for implemented changes (adoption, cycle time, review quality, defect trends)
• Provide enablement support through demos, office hours, and team training sessions

AI Engineer – Developer Productivity

• Assess how engineering teams are currently using AI tools and identify high-value improvement opportunities.
• Evaluate AI-assisted workflows using measures such as quality, speed, adoption, and cost efficiency.
• Build lightweight instrumentation, analysis, dashboards, or automation tools to measure AI usage patterns and support optimization experiments.
• Partner with engineering leads and individual contributors across multiple teams to drive adoption and behavior change.
• Present findings, proposals, and results to engineering leadership; create clear, compelling documentation and slide decks.
• Stay current on model capabilities, pricing, workflow patterns, and optimization techniques.

AI Engineer – AI Efficiency, Economics

• Assess how engineering teams are currently using AI tools and identify high-value improvement opportunities.
• Evaluate AI-assisted workflows and recommend changes that improve outcomes and reduce waste.
• Build lightweight instrumentation, analysis, dashboards, or automation tools to measure AI usage patterns.
• Partner with engineering leads and individual contributors across multiple teams to drive adoption and behavior change.
• Present findings, proposals, and results to engineering leadership; create clear, compelling documentation and slide decks.
• Stay current on model capabilities, pricing, workflow patterns, and optimization techniques.

• Partner with the AI Process Lead and engineering teams to execute prioritized AI productivity initiatives
• Embed with teams to understand day-to-day coding workflows and identify practical improvement opportunities
• Create and maintain team-level AI artifacts, including AGENTS.md , .github/copilot-instructions.md , reusable prompts, and workflow playbooks
• Build and refine AI personas for concrete use cases (for example: PR reviewer, test gap detector, refactoring assistant)
• Implement AI-assisted code review and developer workflow automations (GitHub, CI/CD, scripts, internal tooling)
• Prototype and deliver tools in Python (or similar), with clear documentation and adoption guidance
• Track and report impact metrics for implemented changes (adoption, cycle time, review quality, defect trends)
• Provide enablement support through demos, office hours, and team training sessions

Senior AI Engineer

• Turn quantitative prototypes into reusable tools, services, packaged desktop applications, interfaces, and workflow products that can move from internal demo to client pilot to scaled offer.
• Ship across both cloud-hosted services and locally distributed desktop applications, including Electron-based apps when the workflow or client environment calls for it.
• Build enterprise hardening into the productization layer, including authentication, role-based access control (RBAC), observability, security, release quality, cost controls, and deployment discipline.
• Build evaluation, regression, and release discipline into the productization layer so model logic and agent behavior remain measurable as systems change.
• Work closely with the quant lead so model logic, evaluation intent, and governance requirements survive the move into production.
• Make pragmatic architecture choices across large language models (LLMs), deterministic rules, and hybrid systems based on value, latency, cost, and reliability.
• Help shape repeatable build patterns so strong prototypes become faster, more reliable, and more reusable over time.

QuantAI Engineer – Trading, Investment & Optimization

• Operationalize Data Foundry’s scientific tools and analytical methods into actionable prototypes
• Build the ML deployment pipelines, model serving infrastructure, API layers, and observability guardrails
• Ensure every scientific tool Data Foundry produces are analytics-ready, well-monitored, and exposed through APIs
• Maintain end-to-end ML deployment pipelines: experiment tracking, model versioning, containerized model serving
• Develop model registry infrastructure and feature engineering pipelines
• Implement monitoring and alerting for data pipelines, APIs, ML models, and agentic systems to ensure system reliability and performance at scale
• Productionize predictive and analytical methods from Methods4Insight with versioning and structured error handling
• Build serving infrastructure supporting both synchronous and asynchronous workloads
• Define and implement API contracts, documentation standards, and testing frameworks
• Build and operate cloud-native model serving infrastructure using containers, Kubernetes, and infrastructure-as-code
• Develop CI/CD pipelines for ML models and integrate model serving with Data Foundry’s data pipelines
• Collaborate with the Frontier AI team and Tech@Lilly to ensure Data Foundry’s scientific tools are exposed via well-defined interfaces

Engineer – MLOps, Scientific Platforms

• Apply cutting-edge AI/ML-based Computer Vision algorithms to problems in remote sensing
• Responsible for maturing, optimizing, and deploying algorithms to a cloud-hosted environment
• Serve as a technical leader and guide a team of researchers and developers

• Lead a small team to implement AI/ML algorithms for computer vision problems
• Analyze and pre-process large remote sensing datasets
• Research supervised and unsupervised ATR solutions
• Train, optimize, and deploy deep learning CV models
• Write technical documentation supporting code and user-guides

See all jobs on JobTailor

Lead AI/ML Engineer

AI/ML Engineer

AI Research Scientist, Applied AI

Global GTM & 4P Category Manager – AI Infrastructure

Staff Computer Vision AI/ML Engineer

Senior ML Engineer

Principal AI Engineer, HR Automation

Senior AI Developer – Engineer

Senior Machine Learning Engineer II, Search & Recommendations Ranking

Machine Learning Engineer, Conversion ML

AI Engineer

Software Engineer, Senior Software Engineer – AI Platform

AI Engineer – Developer Productivity

AI Engineer – AI Efficiency, Economics

AI Engineer – AI Efficiency, Economics

Senior AI Engineer

QuantAI Engineer – Trading, Investment & Optimization

Engineer – MLOps, Scientific Platforms

Staff Computer Vision AI/ML Engineer

Senior Computer Vision AI/ML Engineer