AI / AI Engineering Lead

Codiste; Pune District; On-site; INR 2,500,000 - 6,000,000; Full time

Job summary

Codiste is seeking an experienced AI / AI Engineering Lead to bridge client needs and high-quality technical implementation. You will architect and deliver production-level AI solutions, fine-tuned models, and robust data pipelines while mentoring engineers and engaging with clients.

You will lead model training, multi-agent workflows, and CI/CD automation, evaluating new AI frameworks, and ensuring security and compliance across projects.

Qualifications

• 5+ years of software engineering experience with AI/LLM focus.
• 2-3+ years hands-on experience building production AI/LLM solutions.
• Experience in model fine-tuning, data pipelines, and evaluation.
• Experience with multi-agent systems and MCP integrations.
• CI/CD for AI workflows and observability tooling.

Responsibilities

• Lead hands-on model training and fine-tuning for client projects.
• Design multi-agent architectures and MCP integrations.
• Build ETL/data pipelines for training and inference datasets.
• Develop CI/CD pipelines and automated evaluation for AI systems.
• Engage with clients to translate business needs into AI architectures.
• Evaluate emerging AI frameworks and tooling for projects.
• Mentor junior engineers and enforce best practices.
• Ensure data security, privacy, and compliance.

Skills

RAG

Agentic AI

MLops

Python

LLM fine-tuning

Tools

Langfuse

TruLens

n8n

LangGraph

AutoGen

Docker

LangChain

LlamaIndex

FastAPI

Ollama

Job description

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 modern 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.

Required Skills

RAG Agentic AI MLops Python LLM fine tuning

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