AI Engineer (Agentic Solutions & LangGraph)
Primary stack
Nice to have's
Job description
About the Position
Join our team as a Senior AI Engineer specializing in Agentic Solutions and LangGraph to develop and scale autonomous AI systems.
Responsibilities
- Design & Deploy Agentic Systems: Build, test, and scale enterprise multi-agent workflows, stateful reasoning systems, and tool-augmented LLM architectures using LangGraph and LangChain.
- Operationalize & Scale: Develop, integrate, and optimize core algorithmic products and agent microservices into production environments.
- LLMOps & MLOps Infrastructure: Build and maintain CI/CD and LLMOps/MLOps pipelines, including automated evaluations (evals), monitoring, and testing for agent decision-making reliability.
- Cloud Deployment: Package, containerize, and deploy AI models and agentic workflows to cloud endpoints (Azure/GCP).
- Framework Development: Create internal accelerators, reusable agentic design patterns, and engineering frameworks to empower cross-functional project teams.
- Technical Ownership: Drive complex tasks independently with minimal supervision and collaborate closely with global engineering leadership.
Requirements
- 4–7 years of hands-on experience in software, data, or AI engineering, with a primary focus on production AI and agentic systems.
- Deep Agentic AI Expertise: Hands-on proficiency with LangGraph (state graphs, persistence, human-in-the-loop workflows) and LangChain.
- Agent Architectures: Proven experience building multi-agent coordination systems, tool integration/function calling, memory management, and advanced RAG architectures.
- Core Development: Advanced proficiency in Python and building high-performance APIs with FastAPI or Flask.
- Software Engineering Standards: Mastery of software engineering best practices (GitHub, CI/CD pipelines, containerization, unit/integration testing, code reviews).
- Cloud & Orchestration: Cloud experience (Azure or GCP) and familiarity with workflow orchestration tools (Airflow, Kubeflow, or cloud-native orchestrators).
- Data & ML Foundations: Solid foundation in Python-based AI/ML libraries (PyTorch, TensorFlow, Scikit-learn), SQL/databases, and data platforms (Spark/Databricks).
Nice to Have
- Experience with additional agentic frameworks and vector ecosystems (e.g., AutoGen, CrewAI, LlamaIndex, Qdrant, Pinecone, pgvector).
- Experience with LLM evaluation frameworks (e.g., LangSmith, TruLens, Ragas).
- Cloud or AI/ML certifications (e.g., Google Professional ML Engineer, Azure AI Engineer Associate, AWS MLS-C01, Databricks ML Certification).
- Active open-source contributions to AI, LangChain/LangGraph, or data projects.
- Experience working in Agile/Scrum delivery setups for enterprise clients.
We Offer
- Senior Expertise: Direct collaboration with a core team of senior ML/AI professionals (10+ years of experience each).
- Cutting-Edge Tech: Hands-on opportunity to build complex, production-grade Agentic and Generative AI platforms.
- Global Impact: Global project delivery for leading Fortune 500 companies.
- Culture: Dynamic, technology-agnostic environment that values technical autonomy and innovation.
- Growth: Competitive compensation package with the opportunity to shape AI strategy in our expanding India operations.
About the Company
CAREERS
Location & Work Mode
- Location: Bangalore, Karnataka
- Work Mode: On-site
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FMCG,Retail,E-commerce,Healthcare,Telco,Finance,Logistics
About the Company DS Stream is a fast-growing AI & Data consulting company focused on analytics and data management for global brands. The company employs a team of over 200 senior experts who specialize in Data Science, Machine Learning, and MLOps. Their mission is to transform know-how into effective architectures for their clients. Products, Services & Tech Stack Core Solutions Data Engineering: Transforming disparate data into valuable insights for informed business decisions. AI Engineering: Providing modern AI technologies and custom AI tools to support AI-driven business decisions. MLOps: Integrating ML technologies with DevOps practices to automate Machine Learning workflows and improve efficiency. Technologies & Frameworks Specific platforms, languages, or tools are not mentioned in the provided text. Project Scope & Target Clients Client Base Industries Served: FMCG, retail, e-commerce, healthcare, telco, finance, logistics, and more. Engineering Scale The company delivers tailored and complex data analytics for global corporations. Projects include real-time intelligence for the longevity ecosystem, category management for FMCG, and sales automation for resort packages.