About the role:

● Design, build, and maintain scalable backend systems, APIs, and frameworks that integrate Generative AI and other machine learning capabilities.

● Lead the architecture and implementation of robust Retrieval-Augmented Generation (RAG) solutions, utilizing various vector databases and search engine technologies.

● Develop and manage integrations between our core applications and third-party AI services, ensuring seamless data flow and functionality.

● Work extensively with embedding models to represent complex data for use in advanced search, recommendation, and generative tasks.

● Own the MLOps lifecycle for integrated solutions using MLflow, ensuring models and services are versioned, monitored, and reliable in production.

● Adhere to software engineering best practices, including version control (Git), automated testing, CI/CD, containerization (Docker, Kubernetes), and writing clean, well-documented code.

● Collaborate closely with product managers and software engineers to understand application flows and deliver impactful AI-powered features.

● Stay current with the latest advancements in AI services, cloud platforms, and integration patterns to continuously improve our technical stack.

Qualifications & Skills

● Experience: 5+ years of proven, hands-on experience in an ML Engineer, AI Engineer, or Software Developer role with a focus on AI/ML integration.

● Cloud Platforms: Hands-on experience with Azure/ AWS, particularly their AI/ML services (e.g., Azure OpenAI, Azure AI Search, Amazon SageMaker, Bedrock).

● AI/ML & MLOps:

○ Experience with MLflow for managing the ML lifecycle and experiment integration/tracking;

○ Practical experience with Generative AI technologies and RAG architectures.

○ Hands-on experience with vector databases (e.g., Pinecone, Milvus, Weaviate) and search engines (e.g., Elasticsearch, Azure AI Search).

○ Experience with LLM frameworks (e.g., LangChain, LlamaIndex, AutoGen) for building multi-step, tool-using AI systems.

● Programming & Backend:

○ Expert-level proficiency in Python and its core libraries (e.g., FastAPI, Flask, Pandas, Hugging Face).

○ Demonstrable experience building and consuming RESTful APIs.

● Software Engineering: A strong commitment to high-quality, robust software engineering. Must be proficient with Git, code reviews, and writing unit/integration tests.

● Problem-Solving: Excellent analytical skills with the ability to design and architect

complex applications and data flows.

● Education: Bachelor's or Master's degree in Computer Science, Engineering, or a related field is preferred.

Bonus Points (Nice to Have)

● Experience with Databricks.

● Solid backend development experience in a language other than Python (e.g., Go, Java).

● Familiarity with SQL and NoSQL databases.

● Contributions to open-source projects or a strong portfolio showcasing AI integration projects.

● Familiarity with emerging standards for AI interoperability and Model Context Protocols (MCP) for managing state in complex agentic workflows.

● Experience designing and building multi-agent systems where multiple LLM agents collaborate and communicate to solve complex, multi-step problems

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