MAIN AREA OF RESPONSIBILITY:

As an AI/ML Engineer, you will help design, build, and deploy intelligent systems and AI agents that power next-generation experiences across our products. You will work closely with senior engineers and cross-functional teams to implement agentic workflows, orchestration pipelines, and model integrations using frameworks such as LangChain, LangGraph, and other emerging AI toolkits.

Your role will involve developing AI agents, API wrappers, and microservices that connect various systems, enabling contextual reasoning and automation. You will also assist in fine-tuning and deploying machine learning or foundation models where applicable and ensure that AI components are reliable, scalable, and aligned with responsible AI principles.

The AI Chapter owns all AI-specific deployment, observability, and lifecycle operations, and as part of this team, you will support efforts to maintain these pipelines, modernize APIs for AI consumption, and, where required, help implement Model Context Protocol (MCP) or similar interoperability layers to enhance agent-to-system communication.

Responsibilities:

• Contribute to the development and deployment of AI agents and workflow-based systems that autonomously perform reasoning and decision-making tasks.
• Implement and maintain AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar, enabling tool use, memory, and contextual understanding.
• Integrate agents with internal and external APIs, databases, and third-party tools to enable intelligent automation and information retrieval.
• Assist in the development and maintenance of API wrappers or connectors that allow agents to interact with enterprise systems and external services.
• Collaborate with platform and engineering teams to modernize and document APIs, ensuring they are optimized for AI agent interoperability, observability, and security.
   

• Support the design or implementation of Model Context Protocol (MCP) or similar standards to facilitate seamless interaction between agents and systems.
• Fine-tune or adapt custom ML or foundation models for specific use cases and deploy them as part of the agentic pipeline when necessary.
• Support AI-centric DevOps and MLOps workflows, including CI/CD for model services, environment configuration, versioning, and telemetry integration.
• Participate in the monitoring, evaluation, and continuous improvement of deployed AI systems through feedback loops and observability metrics.
• Follow responsible AI guidelines, ensuring fairness, transparency, explainability, and safety in all implementations.
• Collaborate with senior engineers to document designs, improve internal AI frameworks, and maintain clean, production-ready codebases.
   

Requirements:

• 2–4 years of experience in AI engineering, software development, or intelligent systems, preferably in applied AI or workflow automation projects.
• Hands-on experience building or integrating AI agents, chatbots, or intelligent workflows, ideally using frameworks such as LangChain, LangGraph, LlamaIndex, or similar.
• Proficiency in Python (FastAPI) and experience working with modern software development practices (version control, testing, CI/CD).
• Practical understanding of API design and integration, including REST, gRPC, or GraphQL standards.
• Familiarity with machine learning concepts, model fine-tuning, embeddings, and evaluation methods.
• Basic experience with LLM operations, prompt engineering, or retrieval-augmented generation (RAG) setups.
• Exposure to cloud environments (Azure, AWS, GCP) and managed AI/ML services.

• Understanding of DevOps/MLOps fundamentals, including CI/CD, environment automation, and telemetry.
• Strong analytical and problem-solving skills, with the ability to collaborate effectively with cross-functional technical teams.
• Curiosity and willingness to stay updated with emerging agentic AI, orchestration, and interoperability frameworks such as MCP or Semantic Kernel.
   

• Bachelor’s degree in Computer Science, Artificial Intelligence, Engineering, or a related field.
• Experience deploying AI models, agents, or automation workflows into production environments.
• Familiarity with vector databases (e.g., Qdrant, Pinecone, Weaviate) and retrieval-based architectures.
• Knowledge of containerization (Docker), orchestration (Kubernetes), and modern CI/CD pipelines.
• Interest or experience contributing to open-source AI or agentic frameworks.
• Understanding of Responsible AI principles and best practices for human-in-the-loop systems.

Nice to Haves:

• Experience working with Databricks, particularly in:
  • ML workflows: Supporting end-to-end model development, training pipelines, and data preparation using Databricks notebooks and Delta Lake.
  • LLM & Agentic Systems: Exposure to using Databricks for fine-tuning or evaluating large language models, and managing embeddings or feature stores that support agent behavior.
  • Model Gateway: Basic understanding of secure model serving, deployment, and API integration within Databricks environments.
• Hands-on familiarity with MLflow, especially for:
  • Traditional ML: Tracking experiments, managing model versions, and maintaining reproducibility of ML projects.
  • LLMOps / AgentOps: Logging prompt or model variations, monitoring model and LLM performance, and contributing to observability workflows within AI pipelines.