AI Engineer

• Work directly with teams and customers to identify high-impact opportunities for AI and workflow automation.
• Design, build, and deploy AI agents and intelligent systems into production environments.
• Lead discovery sessions to understand workflows, pain points, and business requirements.
• Translate complex business challenges into practical technical solutions.
• Rapidly prototype ideas and iterate based on real-world feedback.
• Integrate AI solutions with existing systems, applications, and data sources.
• Build reusable frameworks and components that accelerate future AI initiatives.
• Monitor adoption, performance, and effectiveness of deployed solutions.
• Collaborate across technical and non-technical teams to deliver meaningful outcomes.
• Stay current with emerging AI technologies and identify opportunities to apply them.

AI Developer

LLM Engineer

• Add AI functionality into existing Python, Node.js, and Ruby codebases
• Build LLM-powered features: chat, summaries, classification, smart search, document Q&A
• Design lightweight RAG pipelines using embeddings and vector search
• Work with vector DBs (pgvector, Pinecone, Qdrant)
• Implement safe, reliable LLM endpoints (OpenAI, Anthropic, Azure)
• Work directly with clients to shape AI features and reduce manual effort
• Advise clients when NOT to use AI and navigate trade-offs around latency, accuracy, and cost

LLM Engineer, Freelancer

• Współpraca nad rozwojem asystenta AI do rejestracji użytkowników na platformie
• Praca nad rozwojem inteligentnej tagowania zainteresowań, aby lepiej dopasować treści do preferencji użytkowników
• Projektowanie i wdrażanie mechanizmów rekomendacji dotyczących personalizowanego planowania podróży
• Tworzenie narzędzi do dostarczania lokalnych wskazówek i informacji o atrakcjach turystycznych
• Analizowanie danych użytkowników w celu ciągłego doskonalenia rozwiązań i funkcji oferowanych przez platformę

AI Engineer – Rola projektowa

• As a Senior Generative AI Engineer, you will lead the design and deployment of advanced AI solutions in cloud environments like AWS, Azure, and GCP.
• Responsibilities include building robust data pipelines, optimizing performance, and deploying large language models (LLMs) for various business needs.
• Emphasizing AI ethics, you'll ensure responsible practices are integrated throughout development.
• Additionally, you'll collaborate with stakeholders to translate business needs into technical requirements and communicate complex AI concepts clearly.

Senior Generative AI Engineer

Machine Learning Engineer

• Research and validate machine learning approaches for DAS-based vessel detection
• Train and adapt deep learning and computer vision models for acoustic signal interpretation
• Build proof-of-concept AI systems integrating DAS and AIS datasets
• Develop scalable ML workflows using Vertex AI and MLFlow
• Analyze vessel signatures including engine, propeller, and vibration patterns
• Collaborate with cross-functional teams to deliver production-oriented R&D outcomes
• Present findings, experiments, and validation results to technical stakeholders.

Senior Machine Learning Engineer – DAS, Acoustic Sensing

• Serve as a key Principal Data & AI Architect – Government Services sector, focusing on pre-sales solutioning and the successful delivery of cloud data and AI solutions
• Collaborate closely with sales and account teams to thoroughly understand client requirements and tailor solutions accordingly
• Lead the Cloud Data Architecture and Delivery teams, ensuring they possess the necessary skills and capabilities to deliver innovative, high-quality solutions that meet sector-specific needs
• Engage in client meetings and workshops to gather insights, refine solution offerings, and present compelling business cases that demonstrate the return on investment (ROI) of our solutions
• Develop and present tailored Go-To-Market strategies and solutions that align with best practices in the Government and Infrastructure sector, particularly in the context of AI and data analytics
• Assume additional technical, organizational, or coordination roles as required to support project success and ensure seamless delivery
• Strategically leverage professional networks to drive innovation, share best practices, and foster a culture of continuous improvement

Principal Data & AI Architect – Government Services

• Build and improve agentic AI workflows for processing technical engineering documentation
• Develop Computer Vision pipelines for diagram and symbol detection
• Design and maintain Knowledge Graph structures representing system components and relationships
• Develop multi-agent orchestration pipelines using modern LLM frameworks
• Integrate retrieval, reasoning, and tool-calling capabilities into AI systems
• Build APIs and backend services supporting AI workflows and experimentation
• Collaborate closely with engineers and researchers in a fast-paced R&D environment

AI/ML Engineer – Agentic AI, Computer Vision

• Designing, building, and deploying AI and Generative AI solutions (e.g., LLM-powered applications, chatbots, intelligent systems)
• Developing and optimizing prompt engineering strategies, including evaluation and iteration
• Building RAG pipelines using vector databases
• Integrating LLMs and AI models into cloud-based applications and APIs
• Implementing agent-based workflows and AI automation solutions
• Continuously researching, evaluating, and applying new AI tools, models, and frameworks

• Drive Technical Excellence: Act as a technical pillar within the Lab, implementing high-standard code and sophisticated system designs. You will mentor mid-level peers and lead deep-dive code reviews.
• Architect Multi-Agent Systems: Design and deploy distributed systems and multi-agent architectures that automate complex engineering tasks. You will own the architectural decisions for "build vs. integrate" strategies.
• Innovate with Agentic AI: Spearhead the evaluation and prototyping of LLMs, Agentic frameworks, and Model Context Protocols (MCPs). You will transform theoretical AI advancements into production-ready tools.
• Own the Full Lifecycle: Take responsibility for the entire development cycle. From initial concept and API integration to production deployment and long-term scalability.
• Influence Product Strategy: Partner with Product and Engineering Managers to ensure the Lab’s innovations align with the broader company roadmap and provide measurable ROI to our developers.

Senior Software Engineer – AI Engineering

• Build production services using Python, FastAPI and Pydantic models.
• Develop APIs and backend workflows that support AI-enabled document and contract intelligence.
• Work with PostgreSQL, SQLAlchemy and Alembic to model, migrate and operate reliable data layers.
• Build and maintain asynchronous job systems, including queue processing, retries, idempotency, dead-letter handling, cancellation, progress tracking and failure recovery.
• Use Redis for caching, transient state, job status and coordination, with sensible reliability patterns such as timeouts, circuit breakers and graceful degradation.
• Build React and TypeScript interfaces that consume backend services cleanly and provide users with transparent job states, useful error handling and polished interaction patterns.
• Use TanStack Query for server state, including query key factories, cache invalidation, optimistic updates and polling patterns for long-running AI jobs.
• Support document-heavy workflows, including ingestion, retrieval, structured extraction, redlining, rich text editing and AI-assisted review.
• Build features that respect enterprise security expectations, including authentication, authorisation, secure data handling and auditability.
• Operate within Azure-based environments, including services such as Blob Storage, Key Vault, managed databases, Redis and containerised deployments.
• Instrument production systems using Application Insights or equivalent observability tooling.
• Use AI coding tools to accelerate implementation, debugging, refactoring, testing and documentation, while maintaining high code quality.
• Improve developer experience by reducing boilerplate, improving test coverage, simplifying local setup and documenting repeatable patterns.
• Move fluidly between infrastructure and product delivery when needed.

Full Stack AI Engineer

• Architect and develop an agentic AI platform. Use this platform to implement the initial agentic workflows capable of automating complex processes in areas including Finance, HR, Legal, Customer Support, and Operations. Mentor more junior agentic AI developers to further expand such flows.
• Work with a variety of AI tools and platforms (e.g., Claude, AWS QuickSight, OpenAI, n8n, LangChain, etc.) to build seamless, integrated solutions.
• Develop and deploy end-to-end solutions that include data ingestion, processing, decision-making, and action execution.
• Fine-tune and customize AI models to meet specific business requirements, ensuring high accuracy and reliability.
• Collaborate with cross-functional teams, including data scientists, engineers, and business stakeholders, to understand requirements and deliver impactful solutions.
• Continuously monitor, evaluate, and optimize the performance of agentic systems to ensure scalability, efficiency, and robustness.
• Maintain clear documentation of system architecture, workflows, and processes. Provide regular updates and reports to stakeholders on system performance and outcomes.
• Stay updated on the latest advancements in AI, machine learning, and automation technologies, and proactively identify opportunities to enhance existing systems.

Lead Agentic AI Engineer

• Solve high-impact problems across product and internal systems, identifying where AI or automation can create real value.
• Work closely with engineering, product, and business stakeholders to understand problems, clarify goals, and define the right approach.
• Design pragmatic, end-to-end solutions from architecture and technical plans to production-ready systems.
• Build systems involving LLMs, integrations, and multi-step or agentic workflows.
• Write production-quality code, build prototypes, and evolve them into robust, scalable solutions.
• Influence technical direction across teams, define best practices, and help others adopt AI effectively.

Principal AI Engineer

• Drive the technical architecture across the domain, with a focus on modernization, scalability and AI integration.
• Lead the design and implementation of microservices and cloud-native systems.
• Guide the transition from legacy systems to modern distributed systems.
• Collaborate with senior stakeholders (EMs, Staff and Principal Engineers, Directors) to align on technology direction.
• Champion engineering excellence, fostering a culture of autonomy, accountability, and quality.
• Provide mentorship and leadership across engineering teams.
• Integrate LLMs and other GenAI models into web applications through efficient API design and implementation.
• Build and optimize API endpoints enabling seamless, real-time communication between front-end applications and back-end AI services.
• Design and develop secure, scalable, and high-performing Java-based microservices for AI model deployment.
• Develop robust back-end systems in Java to support deployment, scalability, and ongoing maintenance of GenAI models.
• Build and maintain data pipelines, including preprocessing input data and post-processing model outputs for application use.
• Implement best practices for sensitive data handling and maintaining high model performance.
• Use Kubernetes and Docker for containerization and orchestration to ensure scalable deployment of AI applications.
• Implement CI/CD pipelines for automated testing and delivery of code changes.
• Maintain scalable and secure cloud infrastructure using platforms such as Google Cloud Platform or Azure for model training, storage, and deployment.
• Utilize vector databases (e.g., Pinecone, Weaviate, Faiss) for embedding management and similarity search.
• Work with frameworks supporting model development and deployment, including Hugging Face, LangChain, and OpenAI ecosystem tools.
• Optimize and fine-tune LLMs based on specific application needs.

Staff AI Engineer

• Design, develop, and deploy autonomous AI agent ecosystems using frameworks such as LangChain, AutoGen, CrewAI, and Semantic Kernel.
• Architect LLM-powered workflows involving multi-agent collaboration, decision logic, memory management, and external tool integration.
• Collaborate with consulting teams to align AI agent solutions with business goals and industry use cases across sectors (FSI, Retail, Manufacturing, etc.).
• Participate in RFI/RFP responses, creating high-impact solution overviews, architectural diagrams, and effort/cost estimations.
• Work closely with AI Strategists, Engagement Managers, and Domain SMEs to define solution blueprints, MVP scopes, and transformation roadmaps.
• Engage in client workshops, demos, and innovation showcases to articulate the potential of Agentic AI and its enterprise applications.
• Contribute to the development of reusable agent templates, accelerators, and reference architectures within Infosys’ AI frameworks.
• Stay current with GenAI advancements, toolchains, and research (LLMs, embeddings, vector DBs, agent planning/reasoning).
• Provide technical mentorship and hands-on support to junior consultants, helping shape internal capability development.
• Collaborate with cross-functional teams on AI governance, responsible AI practices, and integration into enterprise environments.

AI Architect

• Design and implement AI agents using LangChain, CrewAI, or AutoGen for enterprise-grade use cases.
• Develop modular code for task decomposition, memory handling, and tool integration with APIs.
• Collaborate with AI strategists and architects on project design and MVP delivery.
• Conduct testing and fine-tuning of agent behavior using LLM APIs and embeddings.
• Participate in internal code reviews, documentation, and reusable framework building.
• Support pre-sales demos and client innovation sessions with hands-on prototypes.

GenAI Engineer

• Build and maintain production-grade AI agents that take action across ANNA's clinical and operational workflows — scheduling, intake, RCM, clinical decision support — using LLM reasoning over our shared data ontology.
• Design and implement tool use, state management, and failure handling for multi-agent workflows, ensuring reliability and quality in production.
• Contribute to ANNA's MCP server suite, exposing core data and product surfaces — Salesforce, scheduling, RCM, HR — to AI agents in a standardized, composable way.
• Implement and optimize RAG pipelines using pgvector, including embedding strategies and vector search, to surface relevant clinical history at the point of care.
• Build and iterate on clinician-facing product surfaces in React, ensuring they integrate seamlessly with ANNA's AI agent backend and fit the real-world workflows of clinical teams.
• Own features end-to-end: from backend API design in Django/FastAPI through to the front-end experience, with a high bar for quality at every layer.
• Iterate quickly in the open with clinical and operational end users, incorporating feedback and shipping improvements with velocity.
• Contribute to ANNA's clinical data ontology — the shared, specialty-agnostic data foundation that powers all AI agents, decision support, and outcome measurement across the platform.
• Write clean, well-documented, HIPAA-compliant code and contribute to CI/CD pipelines and engineering best practices as the team scales.
• Participate in architecture discussions and help make pragmatic build vs. buy decisions across the stack.

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