Artificial Intelligence Engineer

Lead / Sr AI Developer

You’ll Do

• Architect, build, and optimize production-grade Generative AI applications

using modern frameworks such as LangChain, LlamaIndex, Semantic Kernel,

or custom orchestration layers.

• Lead the design of Agentic AI frameworks (Agno, AutoGen, CrewAI etc.),

enabling intelligent, goal-driven workflows with memory, reasoning, and

contextual awareness.

• Develop and deploy Retrieval-Augmented Generation (RAG) systems

integrating LLMs, vector databases, and real-time data pipelines.

• Design robust prompt engineering and refinement frameworks to improve

reasoning quality, adaptability, and user relevance.

• Deliver high-performance backend systems using Python (FastAPI, Flask, or

similar) aligned with SOLID principles, OOP, and clean architecture.

• Own the complete SDLC, including design, implementation, code reviews,

testing, CI/CD, observability, and post-deployment monitoring.

• Use AI-assisted environments (e.g., Cursor, GitHub Copilot, Claude Code) to

accelerate development while maintaining code quality and maintainability.

• Collaborate closely with MLOps engineers to containerize, scale, and deploy

models using Docker, Kubernetes, and modern CI/CD pipelines.

• Integrate APIs from OpenAI, Anthropic, Cohere, Mistral, or open-source

LLMs (Llama 3, Mixtral, etc.).

• Leverage VectorDB such as FAISS, Pinecone, Weaviate, or Chroma for

semantic search, RAG, and context retrieval.

• Develop custom tools, libraries, and frameworks that improve development

velocity and reliability across AI teams.

• Partner with Product, Design, and ML teams to translate conceptual AI

features into scalable user-facing products.

• Provide technical mentorship and guide team members in system design,

architecture reviews, and AI best practices.

• Lead POCs, internal research experiments, and innovation sprints to explore

and validate emerging AI techniques.

What You Bring

• 7–12 years of total experience in software development, with at least 3 years

in AI/ML systems engineering or Generative AI.

• Python experience with strong grasp of OOP, SOLID, and scalable

microservice architecture.

• Proven track record developing and deploying GenAI/LLM-based systems in

production.

• Hands-on work with LangChain, LlamaIndex, or custom orchestration

frameworks.

• Deep familiarity with OpenAI, Anthropic, Hugging Face, or open-source LLM

APIs.

• Advanced understanding of prompt construction, optimization, and

evaluation techniques.

• End-to-end implementation experience using vector databases and retrieval

pipelines.

• Understanding of MLOps, model serving, scaling, and monitoring workflows

(e.g., BentoML, MLflow, Vertex AI, AWS Sagemaker).

• Experience with GitHub Actions, Docker, Kubernetes, and cloud-native

deployments.

• Are obsessed with clean code, system scalability, and performance

optimization.

• Can balance rapid prototyping with long-term maintainability.

• Excel at working independently while collaborating effectively across teams.

• Stay ahead of the curve on new AI models, frameworks, and best practices.

• Have a founder’s mindset and love solving ambiguous, high-impact technical

challenges.

• Bachelor’s or Master s in Computer Science, Machine Learning, or a related

technical discipline