Role Overview : Assistant Vice President Applied AI Engineer to design and build enterprise-scale AI/ML solutions. The role focuses on Generative AI, Agentic AI, LLM applications, AI evaluation, MLOps, and scalable

AI infrastructure. The successful candidate will bridge AI architecture and hands-on engineering, delivering secure and production-ready solutions while mentoring engineers.

Key Responsibilities

- Design, develop, and deliver production-grade AI/ML solutions and platforms.
- Build agentic AI systems and LLM applications, including multi-agent workflows and tool integrations.
- Develop and optimise multi-step AI workflows and agent orchestration.
- Build AI evaluation frameworks and MLOps tooling for production environments.
- Design scalable APIs and microservices for high-throughput, low-latency workloads.
- Work with product managers, architects, engineers, and business stakeholders to translate

requirements into technical solutions.
- Participate in code reviews and promote engineering best practices.
- Implement secure coding, testing, monitoring, and governance practices.
- Mentor junior engineers and provide technical guidance.
- Drive AI solutions from system design through implementation and production deployment.

Technical Requirements
- 8+ years of software engineering experience, including at least 3+ years building AI/ML systems,

scalable ML infrastructure, or model-serving platforms.
- Strong proficiency in Python, particularly FastAPI and asyncio; experience with Go or Java is also

valuable.
- Production experience developing APIs and microservices.
- Hands-on experience developing Agentic AI and LLM applications, including:
- Multi-agent orchestration
- Tool use
- Planning frameworks
- Multi-step AI workflows
- Strong understanding of prompt engineering and RAG architectures.
- Experience with agentic frameworks such as:
- Strands
- LangGraph
- Google ADK
- Experience with LLM model serving,



including vLLM and OpenAI-compatible APIs.
- Robust understanding of distributed systems, including:
- Microservices
- REST
- gRPC
- OpenAPI
- Event-driven architectures
- Strong AWS experience, including EC2/EKS, S3, IAM, RDS, and at least one AI service such as Bedrock

or SageMaker.
- Hands-on experience with Docker and Kubernetes.
- Experience with CI/CD and Infrastructure as Code, particularly CloudFormation or Terraform.
- Knowledge of observability tools such as OpenTelemetry and distributed tracing.

Preferred / Additional Skills
- Experience building AI evaluation frameworks, including agent benchmarking, reasoning traces, and

production monitoring.
- Experience with:
- Vector databases
- Knowledge graphs
- Semantic search
- AI memory systems
- Retrieval optimisation
- Experience implementing AI safety controls such as:
- Guardrails
- Content filtering
- Prompt injection detection
- PII redaction
- Knowledge of NIST AI RMF, MITRE ATLAS, and OWASP LLM Top 10.
- Familiarity with model fine-tuning techniques such as LoRA, RLHF, and supervised fine-tuning.
- Understanding of Responsible AI, model governance, and autonomous AI security.
- Knowledge of Zero Trust, Entra ID, OAuth2, and OIDC.
- Experience with GPU workload management and inference optimisation, particularly vLLM.
- Financial services or banking experience is advantageous.

Leadership & Stakeholder Skills
- Strong system-design and technical problem-solving capabilities.
- Ability to work independently with limited supervision.
- Strong communication and stakeholder management skills.
- Ability to collaborate with technical and non-technical stakeholders.
- Experience mentoring and guiding junior engineers.
- Ability to manage technical risks and contribute to governance and control frameworks.
- Strong understanding of enterprise technology and AI strategy.

Education
- Bachelor's degree or higher in Computer Science, Engineering, Mathematics, or a related technical

discipline

📌 Assistant Vice President _Applied AI Engineer (Bengaluru)
🏢 Net Connect
📍 Bengaluru