Sr AI/ML Engineer

What You'll Do

• Design, architect, and implement state-of-the-art Generative AI applications and agentic systems using modern AI frameworks such as LangChain, LlamaIndex, or custom orchestration layers.
• Seamlessly integrate large language models (e.g., GPT-4, Claude, Mistral) into production workflows, tools, or customer-facing applications.
• Build scalable, reliable, and high-performance backend systems using Python and modern frameworks to power GenAI-driven features.
• Take ownership of prompt engineering, tool usage, and long/short-term
• memory management to develop intelligent and context-aware agents.
• Deliver high-quality results rapidly by leveraging Cursor and other AI-assisted vibe coding environments for fast development, iteration, and debugging.
• Use vibe coding tools effectively to accelerate delivery, reduce development friction, and fix bugs quickly with minimal overhead.
• Participate in and lead the entire SDLC, from requirements analysis and architectural design to development, testing, deployment, and maintenance.
• Write clean, modular, well-tested, and maintainable code, following best practices including SOLID principles and proper documentation standards.
• Proactively identify and resolve system-level issues, performance bottlenecks, and edge cases through deep debugging and optimization.
• Collaborate closely with cross-functional teamsincluding product, design, QA, and MLto iterate quickly and deliver production-ready features.
• Execute end-to-end implementations and POCs of agentic AI frameworks to validate new ideas, de-risk features, and guide strategic product development.
• Contribute to internal tools, libraries, or workflows that enhance development speed, reliability, and team productivity.

What You Bring

Extensive Python Expertise:

Hands-on experience in Python development, with a focus on clean, maintainable, and scalable code.

• Software Design Principles:

Mastery of OOP, SOLID principles, and design patterns; proven experience designing and

leading complex software architectures.

• GenAI Frameworks:

Practical experience with frameworks like LangChain, LlamaIndex, or other agent

orchestration libraries.

• LLM Integration:

Direct experience integrating APIs from OpenAI, Anthropic, Cohere, or similar providers.

• Prompt Engineering:

Strong understanding of prompt design, refinement, and optimization for LLM-based

applications.

• RAG Systems:

Experience architecting and implementing Retrieval Augmented Generation (RAG) pipelines

and solutions.

• Vector Databases:

Exposure to FAISS, Pinecone, Weaviate, or similar tools for semantic retrieval.

• NLP/ML Knowledge:

Solid foundation in Natural Language Processing and core machine learning concepts

• Cloud & Deployment:

Familiarity with cloud platforms like AWS, GCP, or Azure, including deployment of GenAI

solutions at scale.

• Containerization & Orchestration:

Proficient with Docker, with working knowledge of Kubernetes.

• MLOps / LLMOps Tools:

Experience with platforms such as MLflow, Weights & Biases, or equivalent tools.

• Semantic Search / Knowledge Graphs:

Exposure to knowledge graphs, ontologies, and semantic search technologies.

• Development Lifecycle:Strong grasp of SDLC processes, Git-based version control, CI/CD pipelines, and Agile methodologies.