Lead AI Engineer

Position Overview

We are seeking a technically strong and solution-oriented Lead Applied AI Engineer to support the design and implementation of advanced AI and analytics solutions. This role is ideal for someone with 6+ years of experience in data science and applied machine learning who enjoys combining technical depth with real-world problem-solving. You will work closely with internal technical teams and external clients to translate business needs into scalable AI solutions.

You’ll also guide junior team members, contribute to hands‑on model development, and ensure seamless delivery of analytical components into production environments.

• Architect and implement production-grade agentic AI systems using LLM orchestration frameworks and protocols (e.g., Lang Chain, Lang Graph, Langfuse, MCP).
• Design and deploy Retrieval-Augmented Generation (RAG) pipelines — including chunking strategies, vector store selection, hybrid retrieval, and evaluation.
• Build multi-agent systems with tool use, memory, planning, and inter-agent coordination for enterprise automation and decision-support.
• Evaluate and integrate frontier LLMs (OpenAI, Anthropic, Mistral, open-source) into secure, scalable production architectures, on cloud and on-prem.
• Stay at the forefront of emerging agentic AI research and translate findings into practical product capabilities.

• Design and implement end-to-end ML pipelines: data ingestion, feature engineering, model training, hyper parameter tuning, validation, and deployment.
• Apply classical ML techniques across regression, classification, clustering, time-series forecasting, anomaly detection, and optimization.
• Deliver rigorous data analysis and statistical modeling to generate actionable insights for clients.
• Ensure models are production-ready: robust, interpretable, monitored, and aligned with business KPIs.

• Lead, mentor, and coach junior data and AI scientists — guiding their technical growth, reviewing their code, and building their problem-solving capabilities.
• Define and enforce technical standards, best practices, and review processes across the data science team.
• Drive knowledge-sharing through internal presentations, documentation, and technical sessions.

• Collaborate with client engagement and technical teams to understand business requirements and translate them into actionable AI/analytics solutions.
• Provide strategic input on solution design, aligning analytical capabilities with client objectives.
• Serve as a technical lead in solution delivery discussions and workshops with clients.

• Collaborate with engineering teams to ensure seamless deployment of models into production via APIs, containerization (Docker/Kubernetes), and CI/CD pipelines.
• Implement model monitoring, drift detection, and retraining workflows to maintain solution performance post-deployment.
• Champion clean, maintainable, well-documented code practices across the team.

• Languages & Libraries:
  Python (primary), SQL, Num Py, pandas, scikit-learn, XGBoost, LightGBM
• Agentic & LLM Frameworks:
  Lang Chain, Lang Graph, Model Context Protocol (MCP), OpenAI API, Anthropic API
• RAG & Vector Stores: FAISS, Pinecone; embedding models and evaluation frameworks
• Deep Learning:
  PyTorch, Tensor Flow/Keras, Hugging Face Transformers
• Cloud: one or more of: AWS, GCP, Azure
• Databases:
  PostgreSQL, Oracle; vector databases
• MLOps:
  Docker, MLflow, CI/CD pipelines

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 6–8+ years of hands‑on experience in data science, applied machine learning, and AI engineering.
• Demonstrable production experience with LLMs, RAG pipelines, and agentic AI systems.
• Strong classical ML and statistical modeling expertise across multiple problem types and domains.
• Demonstrated ability to lead technical work and mentor junior team members.
• Ability to balance technical depth with practical delivery and business impact.
• Excellent collaboration and communication skills to work across…