AI Engineer

• Ship and debug code on a live, real-time voice pipeline where latency and correctness are user-facing
• Design control systems around LLMs: guardrails, budgets, watchdogs, safe fallbacks
• Build and operate LLM evaluation and batch-analysis pipelines
• Own traditional ML workflows from data to scheduled production inference
• Trace production issues from a metric anomaly to root cause, including building the evidence when the cause is a vendor

Senior Software Engineer – Applied AI

• Design, build, and deliver AI-powered solutions across a range of engagement types — from sustained strategic initiatives requiring deep discovery and architecture work, to rapid field engagements focused on integrating LOB workflows with enterprise AI platforms and validating feasibility through working prototypes.
• Leverage enterprise AI capabilities include large language models, agentic workflows, cloud computing services (AWS/Azure), and container orchestration (Kubernetes) to solve complex, real-world business problems with a high degree of accuracy and speed.
• Lead application and solution design in partnership with architects, product owners, and LOB technical leads — translating ambiguous business problems into technically sound, implementable solutions with clear production intent from kickoff.
• Own delivery end to end — from scoping and design through development, handoff, and knowledge transfer — maintaining high standards for code quality and solution design at every stage.
• Actively engage with LOB stakeholders to understand not just the problem at hand, but adjacent challenges and emerging needs — surfacing field intelligence that informs the broader AI product roadmap and strategic investment decisions.
• Provide technical guidance and coaching to junior engineers on the team, serving as an informal technical lead within your team — setting the bar for code quality, solution design, and delivery standards without carrying formal management responsibility.
• Demonstrate subject matter expertise across multiple technology disciplines, serving as a credible technical resource for both LOB partners and internal engineering teams.
• Perform analysis, assessment and resolution for defects and incidents of advanced complexity and escalate appropriately.

Software Engineer II – Enterprise AI Products

• Design and develop interactive AI native front-end applications powered by AI Agents/LLMs
• Create intuitive, AI-native user experiences that enable seamless interaction, collaboration, and control across Agentic AI systems and autonomous workflows
• Partner with AI engineering / Data Science teams to integrate APIs, event driven (SSE),polling, webhooks and/or similar features
• Prototype, design and implement modern Agentic UX standards, protocols and ideas , Ex. Elicitation, UX negotiation and A2UI compliant Apps
• Build full stack AI Agents with Google ADK implementing advanced features like A2A, AAIF, A2UI, Agent skills, and MCP
• Research and adopt modern full-stack technologies, Agentic UX innovations, and front-end best practices
• Perform testing, debugging, optimization, and performance tuning to ensure high-quality user experiences
• Partner with product managers, UX designers, and AI architects to translate business requirements into production-ready solutions

AI Engineer – Full Stack

• Design, develop, and deploy machine learning models and AI-powered applications
• Collaborate with cross-functional teams to identify and implement AI/ML solutions for client needs
• Build and maintain data pipelines, model training workflows, and inference systems
• Evaluate and integrate large language models (LLMs), generative AI tools, and emerging AI frameworks
• Monitor model performance, conduct testing, and iterate on improvements
• Document technical architectures, methodologies, and best practices
• Stay current with AI/ML research and translate advancements into practical applications

• Build and maintain shared infrastructure: secure web app hosting infrastructure, MCP/API standards, secret/key management, model selection, guardrails, and a centralized pattern library that all builders across the organization can pull from.
• Own the review, tracking, and advisory process for all corporate AI and automation builds — MCP servers and tool configurations, Claude Skills, API integrations, and agentic workflows — before deployment.
• Partner directly with AI specialists across departments to identify, scope, and build AI and automation solutions that meet high standards for cost, security, and scalability security before they ship.
• Define and maintain the secure intake path for AI and automation requests across the organization, replacing ad-hoc builds and shadow IT with a governed, cost-effective, and scalable process.
• Contribute to the organization’s AI cost efficiency by monitoring, alerting, and setting thresholds on AI spend across usage-based tools and platforms.

AI & Automations Lead

AI Research Scientist

• Conduct fundamental and innovative development in low-cost yet powerful vision-language models (VLM), unified models, automatic model compression, optimization and deployment on cloud and edge.
• Design or implement state-of-the-art techs on model compression, inference speedup, deployment on hardwares, tool automation.
• PoC for various vision+text, generation relevant tasks (VQA, captioning, understanding, etc) and hardwares.
• Contribute to library and tool development to support business; or Publish influential research in top-tier conferences and journals.

Research Intern, Vision Foundation Model and Generative AI

Machine Learning Engineer

• Architect, implement, and maintain scalable data pipelines and feature stores for batch and real-time workloads.
• Build reproducible ML training, evaluation, and inference workflows using modern orchestration and MLOps tooling.
• Integrate event streams from Twilio products (e.g., Messaging, Voice, Segment) into unified, analytics-ready datasets.
• Monitor, test, and improve data quality, model performance, latency, and cost.
• Partner with product, data science, and security teams to ship resilient, compliant services.
• Automate deployment with CI/CD, infrastructure-as-code, and container orchestration best practices.
• Produce clear documentation, dashboards, and runbooks; share knowledge through code reviews and brown-bag sessions.
• Embrace Twilio’s “We are Builders” values by taking ownership of problems and driving them to completion.

• Own a Data, AI/ML product portfolio spanning Growth and Pharmacy Store Operations — delivering insights that advance CVS Health's mission of helping people on their path to better health
• Define and drive the technical roadmap, balancing bold innovation with agile delivery, operational stability, and reliability at enterprise scale
• Define and lead our AI roadmap, architecting the strategic shift from general-purpose LLM deployment to intelligent, agentic automation that drives measurable operational and health outcomes across lines of business
• Lead the strategic shift from rule-based process to AI and agent-based orchestration, enabling intelligent, autonomous workflows across CVS Health's lines of business
• Spearhead the adoption and scaling of the Claude Skills Framework — establishing governance, design standards, and reusable patterns that enable consistent, enterprise-wide AI skill development
• Design and oversee modular Claude Skill modules (SKILL.md, YAML metadata, and execution scripts) to automate high-impact business processes including pharmacy reporting, code deployment workflows
• Champion progressive disclosure strategies for LLM context optimization — mapping specialized Claude Skills to targeted triggers to maximize precision, minimize latency, and deliver scalable enterprise value
• Evaluate emerging agentic AI technologies and frameworks, translating innovations into actionable roadmap investments aligned with CVS Health's strategic priorities
• Architect and oversee the end to end development of Data and ML/AI products following CVS security principles.
• Establish engineering standards and best practices across MCP integrations and Claude Skills Framework development — ensuring consistency, reusability, and alignment with CVS Health's security, data governance, and HIPAA compliance requirements
• Lead hands-on architecture reviews, technical design forums, and solution trade-off discussions across Data Science, Engineering, and Technology teams
• Lead end-to-end AI/ML pipeline integrity — from Claude Skills authoring and MCP server deployment through to production monitoring and continuous optimization
• Ensure engineering standards are maintained across the portfolio — covering solution design, security, and Data/AI Ops practices
• Drive agile delivery within CVS Health's Squad model, managing dependencies across a matrixed environment and removing blockers to accelerate delivery
• Coach and grow engineering leaders and senior individual contributors, building a strong internal talent pipeline that reflects CVS Health's commitment to colleague development and inclusion
• Serve as a trusted technology partner to senior stakeholders across Data Science, Business, and Enterprise Technology teams
• Translate complex technical concepts — including agentic AI architectures, MCP integrations, Claude Skills Framework, and LLM optimization strategies — into clear, business-relevant narratives for Executive and non-technical audiences
• Ensure all data interactions and AI-driven workflows operate in strict adherence to CVS Health's enterprise data governance policies, HIPAA requirements, authentication standards, and role-based access controls
• Partner closely with Information Security and Compliance teams to embed governance into every layer of the AI/ML platform.

Lead Director, AI/ML Engineering

• Contribute to sophisticated research in large language models (LLMs), natural language processing (NLP), and multimodal deep learning
• Help build, implement, and evaluate machine learning models and algorithms
• Participate in data collection, preprocessing, and analysis
• Assist in developing and fine-tuning AI models for various applications
• Create experiments, compare algorithms, tune hyperparameters, and document results
• Share findings with the team, contribute to reports and presentations, and help onboard new team members

AI Research Scientist, PhD – Intern

• Design, train, and evaluate machine-learning models for predicting oral drug absorption–related outcomes from high-dimensional datasets.
• Develop end-to-end ML pipelines, including data ingestion, feature engineering, model training, validation, and performance benchmarking.
• Work with large, diverse datasets, including experimental biopharmaceutics data and clinical pharmacokinetic datasets, and internally generated datasets relevant to predictive modelling.
• Apply and compare a range of ML approaches, including tree-based methods, neural networks, surrogate models, probabilistic approaches for uncertainty-aware prediction.
• Focus on model interpretability and explainability, linking learned patterns to scientifically meaningful drivers where possible.
• Quantify model robustness, generalizability, and uncertainty, particularly in data-sparse or extrapolative scenarios.
• Translate ML outputs into actionable insights for drug development teams, rather than purely academic metrics.
• Communicate results through internal technical reports, cross-functional presentations, and peer-reviewed publications.
• Contribute to the establishment of AI-enabled predictive platforms within Pfizer R&D.

Postdoctoral Scientist – AI & Machine Learning for Predictive Drug Absorption

• Research, experiment with, and implement suitable Generative and ML algorithms, tools and technologies.
• Participate in identifying and assessing opportunities i.e. value of new data sources and analytical techniques and technology, to ensure ongoing competitive advantage.
• Accountable for deployment design, development and maintenance of both traditional ML and AI models.
• Collaborate with partners Enterprise Data, Applied AI, Business, Cloud Enablement Team, and Enterprise Architecture teams
• Delivery of critical milestones for model deployment in the AWS and GCP cloud environments.
• Adopt and promote MLOps best practices to the Data Science community.

AI Machine Learning Engineer

• Research, experiment with, and implement suitable Generative and ML algorithms, tools and technologies.
• Participate in identifying and assessing opportunities i.e. value of new data sources and analytical techniques and technology, to ensure ongoing competitive advantage.
• Review work with leadership and partners on an ongoing basis to calibrate deliverables against expectations.
• Accountable for deployment design, development and maintenance of both traditional ML and AI models.
• Work with junior engineers and peers to provide mentorship and thought leadership.
• Be comfortable presenting new concepts to technical audiences.
• Collaborate with partners Enterprise Data, Applied AI, Business, Cloud Enablement Team, and Enterprise Architecture teams
• Delivery of critical milestones for model deployment in the AWS and GCP clouds.
• Adopt and promote MLOps best practices to the Data Science community.

Senior AI Machine Learning Engineer

• Design and implement scalable AI architectures, including: LLM-powered applications, Retrieval-Augmented Generation (RAG) systems, agentic / multi-step workflows, vector search and retrieval services, model serving and inference layers.
• Establish reusable platform services, APIs, and design patterns to accelerate delivery across multiple teams.
• Build and operationalize AI delivery pipelines: CI/CD for models, prompts, and workflows, prompt versioning and lifecycle management, evaluation and testing frameworks, model and artifact registries.
• Implement monitoring for response quality and hallucination control, latency, throughput, and system reliability, cost observability and optimization.
• Design AI systems with strong controls for data security and privacy auditability and traceability, entitlements and access controls, data lineage and governance.
• Lead delivery of production-grade AI systems with a focus on scalability and reliability, latency and performance optimization, operational readiness and support.
• Partner closely with data engineering and platform teams to integrate AI capabilities with Snowflake and Databricks environments, structured and unstructured data pipelines, APIs and enterprise data services, semantic and knowledge-layer architectures.

Director, AI Engineering – Agentic Platform

• 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

• Design and build multi-agent orchestration patterns (master-orchestrator + specialized worker agents) using protocols such as MCP and A2A
• Lead end-to-end delivery of agentic underwriting and claims automations, from prototype through production hardening
• Partner with IE on orchestration entry-point design (e.g., Azure Function endpoints, master-agent gateways) so the AE/IE seam is clean and scalable
• Lead offshore engineers and team members on agentic patterns, prompt engineering, and reliability practices
• Establish coding, evaluation, and observability standards for agentic systems within the AI & Automation Center of Excellence
• Translate business intent into working agentic systems — not just systems that compile, but systems that deliver measurable business outcomes.

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