AI / AI Engineering Lead
Posted Updated
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.
- 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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