Key Responsibilities:

- Design, implement, and deploy AI agents and generative AI solutions using Python, with hands-on use of LangChain and related orchestration frameworks
- Build modular, scalable, and reusable agent components, including memory, planning, tool use, retrieval, and multi-modal capabilities
- Develop retrieval-augmented generation (RAG) solutions using embeddings, vector databases, and enterprise data sources
- Integrate open-source and proprietary large language models and evaluate the appropriate model for each business use case
- Collaborate with Research, Managed Services, product, data, and technology teams to translate business needs into practical AI solutions
- Prototype and evaluate agent behaviors, prompt strategies, learning approaches, and tool-calling workflows
- Build and maintain scalable data pipelines and APIs that support AI development, evaluation, deployment, and monitoring
- Benchmark solution performance, identify bottlenecks, and implement improvements for quality, reliability, latency, and cost
- Implement observability, testing, evaluation, guardrails, and monitoring to support reliable and responsible AI delivery
- Maintain CI/CD and MLOps/LLMOps practices for controlled and repeatable delivery of AI capabilities
- Follow data governance, security, privacy, and intellectual property requirements throughout the solution lifecycle




- Communicate technical concepts clearly to both technical and non-technical stakeholders and contribute to knowledge sharing

Key Skills:

- Bachelor's degree in computer science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or a related field; advanced degree preferred
- 5 to 8 years of relevant experience in AI/ML engineering, software engineering, data science, or a related technical field
- Demonstrated hands-on experience implementing AI solutions using LangChain. Experience should include building and deploying working solutions, not only theoretical knowledge
- Solid programming experience in Python and experience developing APIs, reusable services, and data pipelines
- Hands-on knowledge of agentic AI patterns, RAG, prompt engineering, embeddings, vector databases, model evaluation, and tool calling
- Experience with one or more cloud platforms such as Azure, AWS, or GCP and containerization technologies such as Docker or Kubernetes
- Experience with CI/CD, MLOps, or LLMOps practices, including testing, deployment, monitoring, and controlled releases
- Ability to explain technical decisions, solve ambiguous business problems, and collaborate effectively with cross-functional stakeholders
- Ownership mindset, curiosity, proactive problem solving, and a commitment to continuous learning and collaboration
- Fluency in English and any additional language relevant to the working market, where applicable

📌 Artificial Intelligence Engineer (Hyderabad)
🏢 Dun & Bradstreet
📍 Hyderabad