Mid AI Machine Learning Engineer (Hybrid)

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.

Minimum Qualifications

• Bachelor’s degree in Computer Science, Artificial Intelligence, Engineering, or a related field is preferred.

• 2–4 years of experience in AI engineering, software development, intelligent systems, or related fields, preferably involving applied AI, workflow automation, or production-grade solutions.

• Hands-on experience building, integrating, or deploying AI agents, chatbots, intelligent workflows, or automation solutions, ideally using frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or similar.

• Strong proficiency in Python, including FastAPI, and familiarity with modern software development practices such as version control, testing, and CI/CD.

• Practical experience with API design and integration, including REST, gRPC, or GraphQL.

• Understanding of machine learning concepts, including model fine-tuning, embeddings, model evaluation, prompt engineering, LLM operations, and Retrieval-Augmented Generation (RAG).

• Familiarity with vector databases and retrieval architectures, such as Qdrant, Pinecone, or Weaviate.

• Exposure to cloud platforms and managed AI/ML services, including Azure, AWS, or GCP.

• Understanding of DevOps/MLOps practices, including CI/CD, environment automation, telemetry, and production deployment.

• Familiarity with containerization and orchestration technologies such as Docker and Kubernetes is an advantage.

• Strong analytical and problem-solving skills, with the ability to collaborate effectively with cross-functional technical teams.

• Knowledge of Responsible AI principles, human-in-the-loop systems, and AI governance best practices is preferred.

• Demonstrated curiosity and willingness to stay updated with emerging agentic AI, orchestration, interoperability frameworks (e.g., MCP), and open-source AI technologies.