Agentic AI Engineer

Role overview

Agentic AI Engineer at Elektrobit Automotive

Overview We are looking for a Senior Expert – Agentic AI Development with 10+ years of software engineering experience and strong expertise in Generative AI and Agentic AI solutions .

Responsibilities

• Build AI assistants, copilots, and workflow automation solutions.
• Develop reusable agent frameworks, skills, and components.
• Perform prompt engineering, evaluation, and performance optimization.
• Collaborate with MLOps teams for deployment, monitoring, and scaling of AI solutions.
• Ensure security, governance, and responsible AI practices.
• Support architecture reviews, technology evaluations, and innovation initiatives.
• Contribute to AI standards, frameworks, and reusable assets.

Employment details

• Location: Bengaluru [India]

Additional role details

• The role focuses on designing, developing, and deploying enterprise-grade AI agents, multi-agent systems, and AI-powered workflow automation solutions that deliver measurable business value.
• Experience - 10years Openings - 1 Responsibilities Agentic AI Development Design and develop AI agents and multi-agent systems for enterprise use cases.
• Implement agent orchestration, planning, memory, reasoning, and tool integration capabilities.
• GenAI & LLM Engineering Design and implement RAG-based solutions using enterprise knowledge sources.
• Integrate and optimize LLMs for business applications.
• Enterprise Integration & Deployment Integrate AI agents with enterprise applications, APIs, databases, and cloud platforms.
• Technical Leadership Mentor engineers and share best practices in Agentic AI development.
• Candidate Profile Experience 10+ years of software engineering experience. 3+ years of hands-on experience in Generative AI/LLM-based applications.
• Proven experience delivering AI solutions in production environments.
• Technical Skills Agentic AI frameworks: LangGraph, LangChain, CrewAI, AutoGen, OpenAI Agents SDK.
• LLMs, Prompt Engineering, RAG architectures, and Vector Databases.
• Strong Python programming and API development skills.
• Cloud platforms (Azure, AWS, or GCP).
• Docker, Kubernetes, CI/CD, and AI deployment practices.
• Understanding of AI governance, security, and observability.
• Success Measures Successful deployment of AI agents into production.
• Improved automation and productivity through AI solutions.
• High-quality, scalable, and secure AI implementations.
• Adoption and business impact of delivered Agentic AI solutions Sravani, Konanki extern.Konanki.Sravani@elektrobit.com