Role Overview

We are looking for an experienced AI / AI Engineering Lead (5 years overall experience, with 2–3 years dedicated to AI/LLM engineering) to join our technical leadership team. In this role, you will bridge client needs and high-quality technical implementation—architecting and delivering production-level AI solutions, fine-tuned models, agentic workflows, and robust data pipelines.

You will act as both a hands-on technical architect and a consultative technical lead who mentors engineers, ensures high solution quality, and engages directly with clients to turn complex business requirements into cutting-edge, secure AI software.

Key Responsibilities

- Model Training, Fine-Tuning &
- Architecture:

Lead hands-on development, fine-tuning (e.g., LoRA, QLoRA, domain adaptation), and evaluation of open-weights and proprietary models tailored to client specifications and production requirements.

- Agentic Workflows &

- MCP Implementation:

Design and orchestrate complex multi-agent architectures, leveraging protocols such as Model Context Protocol (MCP), agentic frameworks, and workflow automation platforms (e.g., n8n, LangGraph, AutoGen).

- Data Engineering &

- ETL Pipelines:

Build, optimize, and maintain high-throughput ETL and data ingestion pipelines for unstructured/structured data processing, vector indexing, and model training/inference datasets.

- AI CI/CD &

- Automated Evaluation:

Implement CI/CD automation pipelines for AI systems, integrating continuous evaluation, automated QA guardrails, benchmarking, and ModelOps telemetry (e.g., Langfuse, TruLens).

- Client Engagement &

- Solutioning:

Collaborate directly with clients to understand business requirements, define technical scope, and design optimal, business-aligned AI architectures.

- Framework Adaptability: Quickly evaluate, adapt, and integrate emerging AI frameworks, vector databases,



and AI infrastructure based on project needs.

- Leadership &

- Mentorship:

Provide technical guidance and mentorship to junior and mid-level engineers, conduct rigorous code reviews, and drive engineering best practices across teams.

- Quality &

- AI Security:

Ensure end-to-end technical quality, performance, safety, and reliability of all client deliverables, enforcing strict data security, privacy, and compliance protocols.

Required Qualifications &

- Skills

- Overall &
- AI Experience:

5 years of total software engineering experience, with 2–3 years of dedicated hands-on experience building, fine-tuning, and deploying production AI/LLM solutions.

- Model Training &

- Fine-Tuning (Must-Have):

Proven, practical experience in model fine-tuning, dataset preparation, hyperparameter tuning, and evaluating local open-weights vs. API-based models.

- Agentic Systems &

- MCP:

Demonstrated experience building multi-agent systems, agentic tool-use pipelines, Model Context Protocol (MCP) integrations, and low-code/orchestration platforms like n8n or custom agent frameworks.

- Data &

- ETL Infrastructure:

Solid background in designing ETL pipelines, data preprocessing, vector database ingestion, and managing data flows for RAG and AI engines.

- AI CI/CD &

- MLOps/ModelOps:

Experience setting up CI/CD pipelines for AI workflows, automated LLM evaluation suites, regression testing, and production observability/monitoring tools (e.g., Langfuse).

- Adaptable AI Tech Stack: Deep practical knowledge across contemporary AI toolchains (Python, PyTorch/Hugging Face, LangChain/LlamaIndex, FastAPI, vector databases, Docker, Ollama/local inference platforms).

- Consultative Communication &

- Leadership:

Strong client-facing communication skills with a track record of translating business problems into technical AI specs and mentoring engineering pods to delivery.

📌 AI / AI Engineering Lead (Pune)
🏢 Coditude
📍 Pune