AI / AI Engineering Lead

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.