Java FSD Gen AI

Java FSD-Gen AI

LTIMindtree is an equal opportunity employer that is committed to diversity in the workplace. Our employment decisions are made without regard to race, color, creed, religion, sex (including pregnancy, childbirth or related medical conditions), gender identity or expression, national origin, ancestry, age, family-care status, veteran status, marital status, civil union status, domestic partnership status, military service, handicap or disability or history of handicap or disability, genetic information, atypical hereditary cellular or blood trait, union affiliation, affectional or sexual orientation or preference, or any other characteristic protected by applicable federal, state, or local law, except where such considerations are bona fide occupational qualifications permitted by law.

We are seeking a highly skilled GenAI Engineer to design, build, and operationalize next-generation AI solutions leveraging Large Language Models (LLMs), AI agents, Retrieval-Augmented Generation (RAG) architectures, and scalable cloud platforms. This role requires strong hands-on expertise across AI concepts, model integration, data pipelines, and MLOps/CICD with the ability to translate business problems into production-grade AI systems.

Key Responsibilities:

• GenAI LLM Engineering
• Design, develop, and deploy LLM-powered applications using leading foundation models (OpenAI, Azure OpenAI, Anthropic, open-source LLMs)
• Build LLM-based AI agents capable of multi-step reasoning, tool use, orchestration, and autonomous workflows
• Implement and optimize agent frameworks (Lang Chain, Llama Index, Semantic Kernel, Auto Gen, CrewAI, etc.)
• Engineer robust prompting strategies, memory mechanisms, and tool-augmented reasoning
• RAG Knowledge Systems
• Design and implement Retrieval-Augmented Generation (RAG) architectures
• Build embedding pipelines using vector databases (FAISS, Pinecone, Weaviate, Azure AI Search, Chroma)
• Optimize document ingestion, chunking strategies, metadata management, and reranking
• Ensure accuracy, relevance, and performance of AI-generated responses
• Machine Learning Model Integration
• Apply practical ML concepts including classification, clustering, ranking, and similarity search where applicable
• Integrate traditional ML models with LLM-based systems for hybrid AI solutions
• Evaluate, fine-tune, and test models using appropriate performance metrics
• Data Engineering Pipelines
• Develop and maintain data pipelines for structured and unstructured data using Python and SQL
• Work with large datasets, APIs, and streaming/batch processing frameworks
• Ensure data quality, lineage, observability, and governance within AI workflows
• MLOps CICD Productionization
• Build CICD pipelines for AI and ML workloads including model versioning and automated testing
• Deploy AI services in containerized environments (Docker, Kubernetes)
• Implement monitoring for model performance drift, latency, and cost
• Ensure security, access control, and compliance for AI systems
• Cloud Platform Engineering
• Design and deploy AI solutions on cloud platforms such as AWS, Azure, or GCP
• Leverage managed AIML services, serverless components, and scalable infrastructure
• Optimize cost, performance, and reliability of AI workloads
• Collaboration Stakeholder Engagement
• Partner with product, platform, and business teams to translate requirements into AI solutions
• Document architectures, design decisions, and operational runbooks
• Provide guidance on GenAI best practices, risks, and responsible AI usage

Required Skills

Experience:

• Core Technical Skills
• Strong proficiency in Python and working knowledge of SQL
• Solid foundation in AIML concepts with hands-on experience deploying models
• Proven experience with LLMs, AI agents, and agent frameworks
• Hands-on expertise with RAG architectures and vector databases
• Experience implementing CICD pipelines for AI or ML systems
• Strong understanding of data pipelines and distributed data processing
• Preferred Good to Have
• Experience fine-tuning LLMs (LoRA, PEFT, RLHF concepts)
• Familiarity with evaluation frameworks for GenAI (hallucination testing, grounding, latency benchmarks)
• Exposure to governance, security, and compliance considerations for enterprise AI
• Background in domains such as BFSI, healthcare, or regulated industries