Gen AI - Sr Software Engineer

Job Description

Location: Bangalore

Experience Required: 8 to 11 years

Position: Senior AI & Agentic Systems Engineer / Senior Software Engineer

This role is designed for passionate individuals eager to advance the frontier of artificial intelligence and software engineering. As a Senior AI & Agentic Systems Engineer, you will play a pivotal role in conceptualizing, designing, and deploying cutting-edge AI solutions that integrate seamlessly within enterprise ecosystems.

Core Attributes:

• Curiosity-driven mindset: A relentless pursuit of knowledge and desire to explore emerging technologies.
• Innovative problem solving: Challenge current paradigms and pioneer new approaches to complex technical problems.
• Adaptability: Thrive in dynamic environments where rapid technological evolution is a constant.
• Ownership & growth-oriented: Demonstrate commitment to continuous learning, quality enhancement, and proactive solution delivery.
• Versatility over specialization: Emphasis on breadth of understanding rather than a narrow skill set tied to specific platforms.

Key Responsibilities

• Lead the design and development of enterprise-grade Large Language Models (LLM) and agentic AI frameworks.
• Develop advanced capabilities including Retrieval-Augmented Generation (RAG), Tool and Function Calling, multi-agent coordination, and secure safe-by-design AI guardrails.
• Translate abstract architectures into detailed, scalable, and maintainable technical designs.
• Integrate AI solutions within complex enterprise AI platforms and knowledge management ecosystems.
• Leverage expertise in fine-tuning open-source AI models and conducting comprehensive performance evaluations.
• Develop scalable, high-performance Python applications, REST APIs, and microservices to support AI workflows.
• Implement robust development practices including CI/CD pipelines, containerization (Docker, Kubernetes), DevSecOps, and automated testing.
• Construct data pipelines for efficient ingestion, transformation, indexing, and retrieval of heterogeneous data (structured, semi-structured, unstructured).
• Design and implement enterprise integration patterns, robust authentication mechanisms, and event-driven architectures.
• Drive AI governance and compliance initiatives, ensuring alignment with privacy, security, risk management, and regulatory standards.

Technical Expertise:

• Deep expertise in AI & agentic systems including prompt and context engineering, tool calling, sustaining multi-agent architectures, and implementing AI safety protocols.
• Proficiency in Python programming with mastery over modern development tools such as Git, Kubernetes, Docker, and CI/CD systems.
• Strong knowledge of data engineering tools and methodologies, vector databases, knowledge graphs, and API design.
• Experience with graph databases like Neo4j, GraphQL interfaces, MCP tooling, and cloud platforms including AWS.
• Hands-on experience with AI ecosystems and platforms such as LangChain, LangGraph, CrewAI, PGVector, and GitHub Copilot.

This role is well-suited for individuals who are not only technically accomplished but who also foster collaborative cross-functional partnerships and uphold rigorous standards of documentation and governance.

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Areas of responsibility

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Gathering of requirements-Assist in conducting workshops and discussions with client stakeholders to gather business process requirements.

Coding and Configuration-"Develop and deliver code for assigned modules, in alignment with client requirements ensuring proper integration with system components.

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Testing and Trial Runs-"Execute testing for assigned modules, validate functionality across required interactions, and participate in User Acceptance Testing (UAT) sessions.

Implementation-Participate in implementation of software application and its integration with other systems to ensure stability.

Maintain Documentation-Prepare detailed reports and documentation on project specifications, activities, and status, while presenting information using flowcharts, layouts, diagrams, code comments, and clear, well-structured code.

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Stakeholder Collaboration-Collaborate with cross functional teams to understand requirements, support development activities, check system compatibility, and ensure solutions meet expected standards.