Sr. D&T Machine Learning Engineer (Agentic Ai)

This team builds enterprise-level scalable and sustainable AI solution pipelines and Agentic workflows to serve the AI and Agentic needs of business-impacting problem statements, maintains cloud and agentic platforms, and explores new complementary tooling for AI and Intelligent automation requirements.

KEY ACCOUNTABILITIES

As part of the team, you will be working as a Sr. Agentic Engineer to operationalize key strategic Agentic and ML Systems ecosystem capabilities.

• Advanced Agentic Platform Enablement: Lead the technical enablement, integration, and continuous enhancement of advanced agentic AI platforms, workflow design, agentic architecture ensuring they adhere to stringent performance, scalability, reliability, and security standards. This includes evaluating new agentic-first platforms, tools and frameworks.
• Custom Connector & Integration Development: Architect, design, develop, and implement robust custom connectors and integration solutions to seamlessly connect diverse data sources, enterprise applications, and external services with our agentic platforms.
• API Design, Testing & Integration: Drive the design, development, and rigorous testing of APIs, ensuring secure, reliable, and efficient integration with both internal and external systems to support agentic workflows.
• Agent Orchestration & Workflow Management: Develop, implement, and optimize sophisticated strategies for orchestrating complex agent workflows, managing inter-agent communication, and enhancing decision-making processes within the platform to achieve desired business outcomes.
• Monitoring, Observability & Reliability Engineering: Strong understanding and working knowledge to collaborate with other engineering teams to establish, implement and support comprehensive monitoring and observability capabilities for Agentic workflows across multiple platforms.
• Knowledge of LLM Ops (Agent quality, telemetry, and FinOps) Working knowledge of end-to-end telemetry for agents (tracing, feedback loops, success metrics, cost and latency dashboards), for driving continuous evaluation of agent quality, safety, and ROI (e.g., task success, deflection, CSAT, cycle-time). Use these insights to tune prompts, tools, and workflows.
• ML Systems Engineering: Research, operationalize, and standardize ML tooling and processes, including installation, maintenance, documentation, and best practices.
• Generative & Agentic AI Architecture Knowhow: Comprehensive knowledge and working experience (preferred) of setting up an Agentic ecosystem with a hyperscaler like Google Cloud Platform (GCP) or Amazon Web Services(AWS).
• Platform Optimization & Lifecycle Management: Conduct ongoing optimization, performance tuning, and lifecycle management of agentic platforms and their underlying capabilities, including bug fixes, security patches, and capacity planning.
• Drive Agile Agentic POCs: Strong working knowledge and experience to develop & lead solution prototypes from ideation, scoping, implementation, and future road mapping with internal and vendor led teams. Drive agile POC execution with focus on hands-on capability validation on new emerging tools and platforms.
• Expert-Level Troubleshooting & Support: Provide advanced technical support and expert-level troubleshooting for complex issues related to agentic platform functionality, integrations, and performance, performing root cause analysis and implementing preventative measures.
• Technical Documentation Excellence: Create and maintain comprehensive, high-quality technical documentation for platform architecture, custom connectors, API specifications, operational procedures to ensure knowledge transfer and system maintainability.
• Governance & Best Practices: Help develop, collaborate, and establish Governance best practices, enterprise standards for Agentic tooling & new agentic solutions.
• Security & Compliance: Ensure all platform development and integrations adhere to enterprise security policies, data privacy regulations, and compliance standards.
• Embrace learning mindset: Continually invest in your own knowledge and skillset through formal training, reading, and attending conferences and meetups.

MINIMUM QUALIFICATIONS
Education: Bachelor’s or Master’s degree in a quantitative field (Computer Science, Software Engineering, Artificial Intelligence, Data Science, etc) or a closely related technical field.

Experience:

• 5-8 years of progressive experience in software engineering, platform development, or a related technical field, with a strong focus on building scalable and resilient systems.
• Minimum of 1-2 years of hands-on, in-depth experience specifically with Generative AI (Gen AI) and Agentic AI technologies, platforms, and frameworks (e.g., LangChain, LlamaIndex, AutoGen, Glean, etc.).
• Proven track record of designing, developing, and deploying complex integrations and custom connectors for enterprise-level applications.

Technical Skills:

• Expert-level proficiency in at least one modern programming language (e.g., Python, Java, Go) with a strong emphasis on writing clean, efficient, and maintainable code.
• Deep understanding and extensive experience with API design principles, development, and rigorous testing (RESTful APIs, GraphQL, gRPC).
• Demonstrated expertise in building custom connectors and integrating diverse systems, including enterprise applications (e.g., Salesforce, SAP, ServiceNow) and various data sources.
• Good understanding/Working experience of agent orchestration frameworks, multi-agent systems, and techniques for managing complex agent interactions and decision flows.
• Extensive experience with major cloud platforms (Google Cloud Platform is preferred) and their AI/ML services (Vertex AI is preferred).
• Proficiency in establishing and utilizing monitoring, logging, and observability tools (e.g., Prometheus, Grafana, ELK stack, Datadog, Splunk) to ensure platform health and performance.
• Working knowledge of MCP connections, RAG, Vectors, Embeddings and Indexing, Knowledge graph and Oauth Flows.
• Strong understanding of CI/CD.
• Strong understanding of orchestration frameworks such as Airflow, Kubeflow etc.

Soft Skills:

• Exceptional problem-solving and analytical capabilities, with a keen eye for detail and a proactive approach to identifying and resolving complex technical challenges.
• Superior communication and collaboration skills, with the ability to articulate technical concepts clearly to both technical and non-technical stakeholders.
• Ability to thrive in a fast-paced, dynamic environment, working both independently and as a key contributor within cross-functional teams.
• Passion for learning new technologies and solving challenging problems.
• Ability to mentor others and lead the team in technology and best practices.

PREFERRED QUALIFICATIONS

• Understanding of the Consumer-Packaged Goods (CPG) industry.
• Familiarity with AI/ML lifecycle stages and MLOps concepts.
• Background in building, maintaining, and supporting traditional ML pipelines in a GCP environment.
• Strong understanding and practical experience with MLOps principles and practices, including CI/CD for AI/ML workflows, model versioning, and deployment strategies.
• Experience with containerization technologies (Docker, Kubernetes) and orchestration platforms.
• Familiarity with Infrastructure as Code (IaC) tools (e.g., Terraform, CloudFormation) is a plus.
• Ability to collaborate cross functional teams and provide tech mentorship to other stakeholders.
• Strong leadership potential with the ability to mentor junior engineers and drive best practices.




ELIGIBILITY

Applicants must meet minimum age qualifications in the country in which the job is located.