Sr. Development Lead

Job Summary

Role Title:

Generative AI Engineer / Applied AI Developer

Experience Required:

Minimum 5+ Years in Software / Machine Learning (with 1.5+ years hands-on in LLMs & GenAI)

Core Tech Stack:

Python, PyTorch, LangChain, LlamaIndex, Vector DBs, FastAPI, Docker, Cloud AI (AWS/Azure)

Target Work Model:

Hybrid (Enterprise Office / Remote flexibility available)

Employment Type:

Full-Time, Permanent Position

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

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

Required Technical Skills & Qualifications

• 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.

Other Requirements

. Candidate Evaluation Matrix (For Technical Interview Panels)

Technical interviewers should evaluate candidates across the following five core technical engineering competencies:

Evaluation Area

Competency Focus

Key Technical Signals

RAG Architecture & Retrieval

Chunking strategies, vector search, hybrid retrieval, and re-ranking

Explains concrete trade-offs between chunk sizes; demonstrates practical experience with hybrid search (BM25 + vector) and cross-encoders to improve recall.

Agentic Loops & Tool Calling

Function calling, stateful graphs, and error recovery mechanisms

Can code and debug an agent with deterministic tool use; understands state management in LangGraph; handles schema parsing errors gracefully.

Prompt Engineering & Parsing

Few-shot prompting, structured JSON schema validation (Pydantic)

Demonstrates advanced prompting techniques (ReAct, CoT); knows how to force LLMs to emit valid JSON schemas reliably using Instructor or Outlines.

Backend & Microservice APIs

FastAPI, asynchronous streaming, connection pooling, and Docker

Comfortably writes async Python endpoints streaming tokens via SSE; explains how to containerize an application and manage GPU/CPU resource constraints.

Evaluation & LLMOps Guardrails

Synthetic benchmarking, hallucination metrics, and guardrail layers

Articulates how to evaluate an LLM app using Ragas or TruLens; explains how NeMo Guardrails or regex/semantic filters prevent prompt injections.