AI / AI Engineering Lead

Role Overview

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.