AI Lead Engineer - Data Modeling

We are looking for a highly capable AI Lead Engineer to contribute to the design and delivery of intelligent, scalable AI solutions. This role focuses on building production-grade systems involving LLMs, vector databases, agent-based workflows, and Retrieval-Augmented Generation (RAG) architectures on the cloud. The ideal candidate should demonstrate strong problem-solving abilities, hands-on technical skills, and the ability to align AI design with real-world business needs.

Responsibilities :

- Collaborate with the Solution Architect to design agentic AI systems (e. g., ReAct, CodeAct, Self-Reflective Agents).

- Build and deploy scalable RAG pipelines using vector databases and embedding models.

- Integrate modern AI tools (e. g., LangChain, LlamaIndex, Kagi, Search APIs) into solution workflows.

- Optimize inference performance for cloud and edge environments.

- Contribute to the development of feedback loops, drift detection, and self-healing AI systems.

- Deploy, monitor, and manage AI solutions on cloud platforms (Azure, AWS, or GCP).

- Translate business use cases into robust technical solutions in collaboration with cross-functional teams.

Requirements :

- Strong coding ability in Python and proficiency in SQL.

- Hands-on experience with vector databases (e. g., Pinecone, FAISS, Weaviate).

- Practical experience with LLMs (OpenAI, Claude, Gemini, etc. ) in real-world workflows.

- Familiarity with LangChain, LlamaIndex, or similar orchestration tools.

- Proven track record in delivering scalable AI systems on public cloud (Azure, AWS, or GCP).

- Experience building and optimizing RAG pipelines.

- Solid understanding of serverless/cloud-native architecture and event-driven design.

- Integrate/expose the AI solution in applications using FastAPI, Flask, and Django.

- Exposure to agentic AI patterns and multi-agent coordination.

- Knowledge of AI system safety practices (e. g., hallucination filtering, grounding).

- Experience with MLOps tools (MLflow, KubeFlow) and CI/CD for ML.

- Understanding of concept/data drift and retraining strategies in production.

- Experience working on AI projects involving classification, regression, and clustering models.

- Prior work on multi-modal AI pipelines (vision + language).

- Familiarity with real-time inference tuning (batching, concurrency).

- Demonstrated ability to deliver a variety of AI solutions in production environments across

domains.

Any Other :

- Bachelor's or Master's degree in Computer Science, Data Science, AI/ML, or related field.

- Strong analytical mindset with a clear focus on business-aligned AI delivery.

- Excellent verbal and written communication skills.