About the role:

As a Senior AI Engineer, you will provide deephands-ontechnicalownershipin designing, building, and operationalizing scalable enterprise-grade Generative AI andAgentic AI solutions that deliver trusted, actionable intelligence across the business. You will helpestablishstrong engineering standards for AI development, including structured experimentation, evaluation rigor, secure deployment, and reliable production operations, ensuring AI capabilities are delivered responsibly and at enterprise quality.

You will lead the end-to-end lifecycle of AIimplementationfrom problem framing and proof-of-concept through production deployment, monitoring, and continuous optimizationembedding governance, observability, performance efficiency, and cost control into solution design.You will lead the end-to-end lifecycle of AI solutionsfrom problem framing and proof-of-concept through production deployment, monitoring, and continuous optimizationembedding governance, observability, performance efficiency, and cost control into solution design& implementation. This is a hands-on builder role requiring true ownership of AI-powered solutions across theproduct lifecycle. The ideal candidate is well-rounded and willing to contribute beyond model development, including data engineering, backend services, application logic, and user-facing components where needed to ensure reliable, scalable production outcomes.

You willcollaborateclosely withAI product lead,Solution &Design Leads, Delivery Leads, platform teams, and global PDT DD&T stakeholders across India, Europe, and the United States to accelerate modern AI adoption and enterprise-wide value realization.

This role reports toDelivery leadand is aligned with theData & Analyticschapter within ICC.

How will you contribute

Gen-AI Solutioning& Delivery

• Design and implement end-to-endLLM-poweredGenerative AI andAgentic AIsolutions, including RAG pipelines, agentic workflows, and multi-step reasoning systems.

• Translate business problems into scalable AI architectures spanning data, retrieval, model orchestration, and application layers.Ensure clear alignment on success metrics & business value with Product leads

• Hands-on leadership& contributionfor end-to-end AI solution development spanning data pipelines, service-layer APIs, application integration, and UI enablement, in addition to model, prompt, and framework engineering, to deliver scalable production systems.

• Define andmaintainreference architectures for GenAI components-RAG pipelines, agentic systems, vector storage, and scalable inference endpoints

• Review solution designs and technical approaches created by junior engineers, ensuring alignment to architectural best practices and enterprise engineering standards.Includes evaluating use of agent frameworks (LangGraph,CrewAI, Swarm),validatingtooling abstractions, memory strategies, andmultiagentcollaboration patterns.

• Deliver in aglobally distributedecosystem, need to collaborateacrossBusiness users,Product lead,Solution & Design leads, Enterprise Architecture teams,Platform teams& other engineering, dev teams

Retrieval, Context Engineering & Agentic Systems

• Build andoptimizeretrieval pipelines using vector databases, hybrid search, reranking, grounding, and provenance tracking.

• Develop agent orchestration patterns, tool integrations, and context-management strategies for long-running or multi-agent workflows.

• Ensure freshness, accuracy, and traceability of knowledge used in AI-generated outputs.

Model Development, Tuning & Optimization

• Evaluate and select foundation models across cloud and self-hosted environments.

• Implement fine-tuning, parameter-efficient adaptation, and prompt optimization strategies.

• Improve latency, scalability, and cost efficiency using batching, caching, distillation, or quantization techniques.

Infrastructure,Deployment & Observability

• Collaborate with Infrastructureand Platformteams to enablescalable, secure, and cost-efficient cloud infrastructuresupporting Generative AI and agentic workloads across development, testing, and production environments.

• Establish automated deployment strategies for Gen-AI services.

• Implement monitoring for quality, hallucinations, drift, latency, and system reliability.Define rollback, governance, and lifecycle management processes for AI and agentic systems

• Ensure strong GenAI observability across all solutions, including prompt tracing, metrics instrumentation, log reduction, quality monitoring, and operational telemetry.

Engineeringownership & excellence

• Design, build, andmaintainscalable data pipelines, backend services, and APIs that power AI-driven applications and production workflows.

• Contribute to frontend or user experience components where needed to deliver intuitive, reliable, and production-ready AI solutions.

• Apply strong software engineering best practices across theproduct implementation lifecycle, including version control, CI/CD automation, testing strategies, code quality, and comprehensive documentation

• Demonstrate flexibility to contribute beyond core GenAI development, supporting adjacent engineering domains to ensure true end-to-end solution delivery and operational success.

Responsible AI, Security & Compliance

• Adhere toprivacy, security, and ethical AI standards across data, prompts, models, and outputs.

• Implement guardrails, content filtering, access control, and human-in-the-loop review whererequired.

• AI solutionsalignedwith regulatory expectations and enterprise governance frameworks.

• HIPAA/GDPR audits; model cards documenting bias testing (demographic parity <0.8), lineage tracing.

• PII anonymization, access controls (RBAC/IAM) for datasets/models.

• Protect against adversarial attacks (prompt injection, evasion); robustness testing (Adversarial Robustness Toolbox)

Innovation& Enablement

• Continuously evaluate emerging Gen-AI technologies and guide adoption strategy.

• Act as a strong advocate for responsible and high-impact AI adoption acrossthe team

• Educate stakeholders on AI capabilities, limitations, and best practices, enabling informed and ethical usage.

• Drive reusable AI patterns, playbooks, and internal enablement programs to acceleratescalability &maturity.

• Mentorpeers, fostering a culture of experimentation, learning, and engineering excellence in AI.

• Contribute to shapingAIlong-term capability roadmap.

Requirements:

• Bachelor's degree in computer science, Engineering, Data Science, or related field.

• 6+ years of relevant experience in Data Science, Machine Learning, AI engineering.

• Hands-on experience building and deploying Generative AI or Agentic AI solutions in enterprise environments, from POC through production.

• Proven hands-on experience building and deploying AI/ML solutions using Databricks and AWS, with CI/CD automation through GitHub Actions for reliable, production-grade releases.

• Strongproficiencyin Python,modern ML frameworks such asPyTorchor TensorFlow, modern AI orchestration frameworks such asLangchain,Langflow

• Demonstrated experience delivering production-grade AI-powered applications end-to-end,contributions in aspects includingdata engineering, backend services, and application or UI components.

• Deep understanding of NLP, transformers, embeddings, vector databases, and RAGarchitecture.Experience deploying scalable AI services on cloud infrastructure. Strong knowledge of monitoring, model lifecycle management, and evaluation frameworks.

• Understanding of Responsible AI, privacy, security, and governance in regulated environments.

• Demonstrated ability to manage AIsolutions forscalability, reliability, and operational cost efficiency.

• Excellent problem-solving, communication, and cross-functional collaboration skills in global teams.

Preferred Skills:

• Relevant AI focused professional degree, certifications

• Experience designingand implementingmulti-agent AI systems

• Experience defining AI Total Cost of Ownership (TCO), ROI measurement, and enterprise adoption strategy.

• Proventrack recordof mentoring teams and championing modern AI engineering practices.

• Background in healthcare, life sciences, or other regulated industries.

• Experience in MCP, Agent2Agent communication protocols