The Role
Leads the architecture and delivery of production AI and LLM solutions, including model fine-tuning, multi-agent workflows, MCP integrations, ETL pipelines, vector indexing, AI CI/CD, automated evaluation, and ModelOps observability. Partners with clients to translate business needs into secure technical solutions, mentors engineers, conducts code reviews, evaluates emerging frameworks, and ensures solution quality, performance, safety, privacy, and compliance.
Summary Generated by Built In
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.
Skills Required
- 5+ years of total software engineering experience
- 2-3+ years of hands-on experience building, fine-tuning, and deploying production AI or LLM solutions
- Practical experience with model fine-tuning, dataset preparation, hyperparameter tuning, and evaluating open-weights versus API-based models
- Experience building multi-agent systems, agentic tool-use pipelines, and Model Context Protocol integrations
- Experience with n8n or custom agent frameworks
- Experience designing ETL pipelines, data preprocessing, vector database ingestion, and RAG or AI data flows
- Experience setting up AI CI/CD pipelines, automated LLM evaluation suites, regression testing, and production observability
- Knowledge of Python, PyTorch or Hugging Face, LangChain or LlamaIndex, FastAPI, vector databases, Docker, and Ollama or local inference platforms
- Strong client-facing communication and ability to translate business problems into technical AI specifications
- Experience mentoring engineering teams or pods and driving delivery best practices
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The Company
What We Do
Coditude stands out as a rapidly growing force in the digital realm, offering straightforward, impactful tech capabilities. Our team, both seasoned and savvy, is the perfect ally to thrive in the digital age. We excel in Product Development, SaaS Solutions, Enterprise Mobile Applications, AI, Cloud Solutions, Browser Extension Development, and Digital Commerce Solutions, not to mention our prowess in Infrastructure Modernization and Management
