Job Summary

We are hiring an Agentic AI Engineer to build and ship production AI systems as part of BayOnes AI Strategy and Innovation Office. The role sits within a team-oriented, distributed engineering team and contributes to both internal projects and solutions developed for the practices client portfolio. Our portfolio extends across many industries and business areas, and the work shifts across technical areas from one engagement to the next. The role requires adaptability, breadth of thinking, and the discipline to bring rigorous engineering to every deliverable. The Agentic AI Engineer is personally accountable for the quality and reliability of every deliverable produced, on both internal and client engagements. The work spans agent-based systems, web applications, data engineering, and infrastructure, with agentic pair programming as the primary working mode. Engineering discipline, sound technical judgment, and adherence to the teams quality standards are expected on every piece of work.

The ideal candidate is currently working with agentic AI systems in a hands-on capacity and has a track record of shipping production systems. Professional experience extends beyond generative AI alone, with a broader engineering foundation that demonstrates range across problem types. The role calls for an engineer who delivers consistently, owns what they ship, and brings the same rigor to unfamiliar problems as to familiar ones.

Responsibilities

- Agentic AI Development (45%): Build AI systems for internal and client engagements, from initial POC through production delivery.
- Agentic AI Development (45%): Design and implement agent-based solutions for complex, multi-step problems across the practices diverse engagement portfolio.
- Agentic AI Development (45%): Build POCs and demos that demonstrate technical feasibility and value to stakeholders.
- Agentic AI Development (45%): Evaluate and select the right approach for each problem, including knowing when an agent-based approach is appropriate and when a simpler method fits.
- Agentic AI Development (45%): Participate in system design within the team, contributing architectural thinking and implementation expertise.
- Agentic AI Development (45%): Own the full lifecycle of systems built: design, implementation, evaluation, and reliability.
- Engineering and Development (25%): Build and maintain web applications, APIs, and backend services for internal and client engagements.
- Engineering and Development (25%): Design and work with database schemas, write and optimize queries, and manage data across relational and vector database systems.
- Engineering and Development (25%): Build data pipelines and integrations across the practices data platforms.
- Engineering and Development (25%): Contribute to infrastructure work, including containerized deployments and cloud configuration.
- Engineering and Development (25%): Move between technical areas with engineering discipline, applying the same rigor regardless of the specific technology or context.
- Engineering and Development (25%): Deliver work that meets the teams standards across both internal initiatives and solutions developed for the practices client portfolio.
- Quality and Delivery Excellence (20%): Write and maintain tests (unit, integration, end-to-end) as part of every deliverable.
- Quality and Delivery Excellence (20%): Participate in code reviews with substantive technical feedback, both giving and receiving.




- Quality and Delivery Excellence (20%): Maintain documentation for systems built, including architecture decisions, setup instructions, and integration points.
- Quality and Delivery Excellence (20%): Apply evaluation discipline to agent systems, including structured evaluation harnesses and observability for deployed systems.
- Quality and Delivery Excellence (20%): Meet delivery commitments on time and communicate accurately on progress and blockers.
- Quality and Delivery Excellence (20%): Uphold the teams code quality standards, testing practices, and documentation requirements across all work.
- Growth and Collaboration (10%): Stay current with developments in the agentic AI landscape, language-model orchestration, and the broader AI engineering field.
- Growth and Collaboration (10%): Learn new tools, frameworks, and patterns as the practice adopts them, working from reference implementations and team guidance.
- Growth and Collaboration (10%): Actively expand technical skills and depth across the practice's engineering domains, pursuing breadth as the engagement portfolio evolves.
- Growth and Collaboration (10%): Contribute to team knowledge by sharing findings from new tools, techniques, and engagement experiences.
- Growth and Collaboration (10%): Incorporate feedback from code reviews, system evaluations, and team retrospectives into ongoing work.
- Growth and Collaboration (10%): Collaborate effectively within the distributed team, including responsive communication and proactive escalation of blockers.

Required Qualifications

Engineering Experience

- Professional engineering experience that extends beyond generative AI, demonstrating a broader technical foundation.
- Currently or recently working with agentic AI systems in a hands-on capacity.
- Experience building POCs, demos, or production systems using agentic methods.

Technical Breadth

- Demonstrated range across engineering disciplines, comfortable moving between generative AI development, web development, database work, and infrastructure as the practices engagements shift.

Technical Requirements

- Python as primary language.
- Microsoft Azure required as the primary cloud platform.
- Hands-on experience building with Azure AI Foundry.
- Claude Code experience and expertise required, including hands-on development of skills and plugins on the platform. This is non-negotiable for the role.
- LangGraph experience required. Demonstrated ability to design state graphs, conditional edges, and multi-agent compositions.
- Model Context Protocol experience required. Comfortable designing tool calls and building protocol wrappers.
- Agentic pair programming with generative AI as the primary working mode. Prior experience is required.
- Pydantic for data validation and structured outputs across agent systems and APIs.
- SQL proficiency and PostgreSQL experience.
- Vector database experience (pgvector, Azure AI Search, or similar).
- Familiarity with modern data platforms such as Snowflake and Databricks.




- Multi-step agent systems with proper evaluation and validation.
- Strong fluency across modern frontier language models such as the GPT, Claude, and Gemini model families. Model selection is driven by the requirements of each task and the practical considerations of cost.
- The discipline to choose mature, proven frameworks for AI engineering work. Technical decisions are grounded in clear engineering justification and supported by data.
- Docker and containerization for development and deployment workflows.
- FastAPI or equivalent web frameworks for building APIs and backend services.
- Working knowledge of GitHub workflows, code review discipline, and infrastructure-as-code patterns.

Working Style

- The candidate's professional identity centers on engineering practice. The role centers on building and delivering systems that meet production standards, and every deliverable is expected to reflect engineering discipline.
- Adaptable and comfortable with ambiguity, able to take on unfamiliar work and deliver with the same discipline as familiar work.
- Capacity to step into new problem shapes and reason from first principles.
- Personal accountability for the quality and reliability of all delivered work.
- Excellent communication skills, both written and verbal, with clear and accurate communication on project status, blockers, and progress.
- Strong risk discipline, demonstrated by early recognition of trouble signals and appropriate escalation. Defaults to proven, battle-tested infrastructure for production work.
- Operates within the team's established technical direction. Architectural decisions and technology choices align with that direction.
- Considers AI-specific constraints during design and delivery, including ethical, legal, and privacy concerns, and ensuring best practices of responsible AI engineering are preserved.

Education

A Bachelors degree in any branch of engineering or related fields is strongly preferred as the educational baseline for the role. Equivalent demonstrated experience will be considered.

Preferred Qualifications

- Although Microsoft Azure is the primary platform, Google Cloud Platform experience is also valuable and applicable to the practice's work.
- Familiarity with Azure Container Apps, Azure Kubernetes Services, or similar container orchestration platforms.
- Familiarity with Apache Software Foundation tools such as Apache Airflow, Apache Kafka, and Apache Flink.
- Experience with LangChain, PyTorch, or other AI and machine learning frameworks.
- CrewAI or other multi-agent frameworks.
- LlamaIndex for RAG and data ingestion workflows.
- Celery and Redis for background task processing and caching.
- Familiarity with A2A (Agent-to-Agent) protocol for inter-agent communication.
- Experience with modern natural language processing tools, including embedding models and entity recognition.
- Familiarity with vision-language model integration for multi-modal AI use cases.
- Experience working in regulated markets, including the compliance and risk-management disciplines suchenvironments require.
- Playwright for end-to-end testing automation.
- Familiarity with hybrid architectures that integrate deterministic and generative AI techniques.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Agentic AI Engineer -Bayone (Gurugram)
🏢 BayOne Solutions
📍 Gurugram