Job Summary

We are seeking a talented, technically passionate Generative AI Engineer with 3+ years of professional software engineering and applied AI/ML experience to design, build, and deploy production-grade LLM applications. In this hands-on engineering role, you will turn the latest advancements in generative artificial intelligence into resilient, scalable business solutions.

You will specialize in constructing Advanced Retrieval-Augmented Generation (RAG) pipelines, engineering autonomous agents with deterministic tool-calling capabilities, optimizing prompt workflows, experimenting with parameter-efficient model adaptation (LoRA/QLoRA), and serving high-performance inference APIs via containerized microservices hosted on AWS or Microsoft Azure.

Key Responsibilities

Key Responsibilities & Core Engineering Mandates

A. RAG Architecture & Vector Search Engineering

- RAG Pipeline Development: Design and implement end-to-end RAG pipelines incorporating multi-modal document parsing, recursive text splitting, and semantic chunking.
- Vector Store Management: Integrate, optimize, and benchmark vector databases (Pinecone, Qdrant, Chroma, Weaviate, pgvector, Azure AI Search) for sub-second similarity search.
- Hybrid Search & Re-ranking: Implement hybrid retrieval combining dense vector embeddings with sparse lexical keyword matching (BM25) and cross-encoder re-ranking models (Cohere Rerank, BGE).
- Context Optimization: Engineer contextual compression, parent-document retrieval, and metadata filtering schemas to eliminate context noise and hallucinations.

B. Agentic Workflows & Multi-Tool Integration

- Agentic Frameworks: Build stateful multi-agent workflows and autonomous task execution loops using LangChain, LangGraph, LlamaIndex, or AutoGen.
- Tool Calling & Action APIs: Develop robust, type-safe function-calling and tool-use interfaces connecting LLMs to external REST APIs, relational databases, and enterprise systems.
- Loop Safeguards & Routing: Implement plan-and-solve agent patterns, multi-step reflection loops, and fallback error-handling to prevent infinite agent recursion.

C. Prompt Engineering, Output Validation & Fine-Tuning

- Prompt Engineering: Design and test systematic prompt templates utilizing Chain-of-Thought (CoT), Few-Shot prompting, ReAct patterns, and directional stimulus prompting.
- Structured Output Parsing: Enforce deterministic, validated output structures using Pydantic, Instructor, Guidance,



or Outlines to guarantee reliable downstream ingestion.
- Model Adaptation: Conduct Parameter-Efficient Fine-Tuning (PEFT, LoRA, QLoRA) on open-weights models (Llama 3, Mistral, Phi-3/4) for domain-specific tasks.

D. Backend API & Inference Microservices

- Inference APIs (FastAPI): Develop high-throughput asynchronous backend microservices using Python (FastAPI), managing connection pooling, concurrency, and rate limiting.
- Streaming Latency: Implement low-latency streaming inference pipelines utilizing Server-Sent Events (SSE) and WebSockets for conversational user interfaces.
- Inference Engines: Optimize inference throughput utilizing modern serving engines like vLLM, TensorRT-LLM, or Ollama, minimizing Time-to-First-Token (TTFT).
- Containerized Deployment: Containerize applications with Docker and configure deployment manifests for Kubernetes, AWS ECS/EKS, or Azure Container Apps.

E. Evaluation, Guardrails & LLMOps Observability

- Automated Model Evaluation: Establish automated evaluation benchmarks utilizing synthetic test datasets and evaluation harnesses (Ragas, TruLens, DeepEval) to quantify faithfulness and recall.
- AI Guardrails & Safety: Implement enterprise guardrails (NeMo Guardrails, Llama Guard, Azure Content Safety) to proactively mitigate prompt injection, jailbreaks, and toxicity.

Observability & Tracing: Integrate comprehensive LLM tracing, prompt versioning, and cost monitoring using tools like Langfuse, Phoenix, or Weights & Biases

Skill Requirements

- Total Experience: Minimum 3+ years of professional software engineering / data science experience, with at least 1.5+ years of dedicated hands-on GenAI / LLM development.
- Education: Bachelor’s degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, or equivalent technical field.
- Python & ML Foundations: Advanced Python programming skills (asyncio, type hints, Pydantic, pytest) and deep familiarity with PyTorch or Hugging Face Transformers.
- GenAI Frameworks: Extensive hands-on experience with LangChain, LangGraph,



LlamaIndex, and commercial/open foundation model APIs (OpenAI, Anthropic, Bedrock).
- Vector Search & RAG: Proven track record building and deploying production RAG architectures using vector databases (Pinecone, Chroma, Qdrant, pgvector).
- Backend & Containers: Hands-on proficiency developing asynchronous REST APIs using FastAPI, Docker containerization, and Git-based collaborative workflows.
- Core Competencies: Strong analytical problem-solving skills, solid understanding of transformer architecture mechanics, and proactive technical curiosity.

4. Preferred Qualifications & Industry Certifications

- Inference Optimization: Hands-on experience with model quantization (GGUF, AWQ, GPTQ) and high-throughput inference serving engines (vLLM, TGI, TensorRT-LLM).
- Cloud Certifications: Cloud AI certifications: AWS Certified AI Practitioner / ML Specialty, Microsoft Certified: Azure AI Engineer Associate (AI-102), or Azure AI Fundamentals.
- Open Source & Portfolio: Demonstrated contributions to open-source GenAI projects, LangChain/LlamaIndex community plugins, or active technical blog/GitHub repository.
- Agile Collaboration: Experience working in Agile/Scrum sprint environments collaborating with Product Managers, UX Designers, and Frontend Developers.

5. Key Performance Indicators (KPIs) & Success Benchmarks

Success and engineering performance in this role will be measured against the following technical benchmarks:

- 1. Retrieval Accuracy: RAG Faithfulness & Retrieval Relevance: Achieving 90% ground-truth accuracy and minimal hallucination scores on benchmark evaluation datasets.
- 2. Low Latency: Inference Latency SLAs: Maintaining p95 streaming Time-to-First-Token (TTFT) < 800ms and total request latency within defined application budgets.
- 3. Security & Safety: System Robustness & Guardrails: Zero critical prompt injection bypasses or unauthorized data leakage incidents in production environments.
- 4. Engineering Quality: Code Quality & Test Coverage: Delivering modular, well-documented code with 80% automated unit/integration test coverage across API endpoints.

5. Delivery Velocity: Sprint Execution Velocity: Consistently completing committed sprint stories and technical spikes on schedule with minimal defect slippage

Must Have Skill

Advanced Business Analysis - Gen AI

Valuable to Have Skill

Other Requirements

📌 Senior Developer (DPO) (Noida)
🏢 HCLTech
📍 Noida