Senior Generative AI / Applied AI Engineer

Experience: 5+ Years

Location: Hyderabad / Remote – India

Job Type: Full Time

Relocation: Allowed

About the Role

We are looking for a hands-on Senior Generative AI / Applied AI Engineer with strong expertise in LLMs, RAG, AI Evaluation, Context Engineering, Model Optimization, AI Agents, Multimodal AI, and Production AI Systems . The role involves building and deploying scalable AI solutions for a next-generation healthcare platform and clinical assistant .

Key Responsibilities

- Design, develop, deploy, and optimize LLM-powered healthcare applications and AI services using Python .
- Work with OpenAI, Anthropic, LLaMA, Mistral , and other foundation/reasoning models.
- Build production-grade RAG pipelines covering ingestion, document processing, chunking, embeddings, indexing, semantic/hybrid search, retrieval, reranking, grounding, and source attribution.
- Work with vector databases such as FAISS, Pinecone, Qdrant, Weaviate, Milvus, and pgvector .
- Implement prompt engineering, structured outputs, function/tool calling, reasoning workflows, AI agents, and multi-step workflows .
- Build AI evaluation frameworks covering accuracy, relevance, groundedness, hallucination, consistency, safety, retrieval quality, latency, cost, and model stability .
- Implement LLM-as-a-Judge, deterministic/semantic evaluations, retrieval metrics, benchmarking, and human/clinical review .
- Design AI safety, reliability, guardrails, hallucination reduction, prompt-injection protection, PHI/PII protection, validation, fallback, and human escalation mechanisms.
- Develop context engineering, context retrieval, short/long-term memory, patient-context memory, memory retrieval, and summarization capabilities.
- Manage AI releases, regression testing, prompt/model/configuration versioning, A/B testing, canary releases, controlled rollouts, and rollback mechanisms .
- Establish LLMOps and production AI observability , monitoring quality, latency, cost, token usage, retrieval failures, model failures, hallucinations,



safety events, and user feedback.
- Develop multimodal AI solutions involving text, images, PDFs, documents, tables, and structured healthcare data.
- Build and evaluate AI agents, agent orchestration, tool/function calling, reasoning workflows, and human-in-the-loop systems using frameworks such as LangGraph, AutoGen, CrewAI, and LangChain .
- Work with healthcare data including EHR/EMR, clinical notes, patient records, care plans, lab information, medications, diagnoses, CSVs, and clinical knowledge bases .
- Apply healthcare interoperability standards such as FHIR, HL7 and clinical terminologies including SNOMED CT, ICD, and UMLS .
- Deploy and operate production AI systems on AWS , including Bedrock, SageMaker, ECS/EKS, Lambda, S3, RDS/Aurora, OpenSearch, API Gateway, CloudWatch, IAM, and Secrets Manager .
- Work with Docker, Kubernetes, CI/CD, autoscaling, observability, security, high availability, cost optimization, and production troubleshooting .
- Continuously benchmark emerging foundation, reasoning, embedding, reranking, multimodal, and long-context models and recommend production adoption based on quality, reliability, latency, scalability, and cost.

Must-Have Skills
- 5+ years Software Engineering / ML Engineering / Applied AI experience.
- Strong hands-on Python development.
- Production experience building LLM-powered applications and RAG systems .
- Strong knowledge of LLMs, Transformers, Embeddings, RAG, Prompt Engineering, Context Engineering, NLP, Vector Search, and AI Evaluation .
- Experience with commercial/open-source LLMs and vector databases .
- Experience with LangChain and/or LlamaIndex .




- Experience with Hugging Face / Transformers .
- Strong backend/API engineering fundamentals.
- Docker/containerization and cloud deployment experience.
- Experience with AI evaluation, monitoring, Git, and contemporary software development practices .

Strongly Preferred AWS, Amazon Bedrock, Healthcare/HealthTech, US Healthcare, HIPAA, PHI/PII, EHR/EMR, FHIR, HL7, LLMOps, Kubernetes/EKS, LangGraph, Agentic AI, Multimodal AI, Production AI Observability, Clinical Terminology/Ontologies.

Candidate Profile

Looking for hands-on engineers who can design, code, deploy, evaluate, troubleshoot, and continuously improve production LLM systems . Candidates should understand the trade-offs between quality, reliability, latency, scalability, and cost , and be capable of independently owning production AI systems.

Not suitable for: candidates whose experience is limited to basic ChatGPT/API integrations, simple chatbots, prompt engineering without software engineering, AI POCs without production ownership, or certifications without hands-on production experience.

Interview Expectations

Candidates should be able to explain a production LLM/RAG system they personally built or significantly owned, including architecture, model selection, RAG, chunking, embeddings, vector database, retrieval/reranking, prompts, context engineering, evaluation, hallucination management, guardrails, cloud architecture, deployment, monitoring, scaling, latency, cost, security, and troubleshooting .

Soft Skills

- Strong analytical and problem-solving ability
- Clear written and verbal communication
- Strong documentation
- Independent ownership
- Bias for action with reliability in mind
- Collaboration with Product, Clinical, Engineering, QA, and DevOps teams
- Comfortable working in a fast-paced environment and with global teams

Work Location Hyderabad / Remote | India | Flexible working hours for global collaboration

Remote-friendly with optional Hyderabad base.

📌 Senior Generative AI Engineer (Hyderabad)
🏢 Ekshvaku Tech Innovations
📍 Hyderabad