Job Summary

About the role As an Agentic Forward Deployed Engineer, you operate at the front line of delivery - embedded with the client, turning ambiguous business problems into production agents, fast. Your deliverable is Business Transformation Agents: autonomous and multi-agent systems that automate and reimagine real business processes such as invoice disputes, procurement approvals, onboarding, claims and compliance workflows. You own each agent end to end -conceptualize, build, integrate, evaluate, deploy, and sustain - and you lead a small team to do the same. You build exclusively in Python using agent development kits, and you bring Agentic AI capabilities to life inside the client's world, with Responsible AI, evaluation and security as non-negotiables. Technology mandate Language: Python preferable Frameworks: Agent Development Kits (ADKs) ; e.g. Google ADK, LangGraph, CrewAI, OpenAI Agents SDK, AWS Bedrock AgentCore, Microsoft Agent Framework / Semantic Kernel. Framework choice follows the engagement; the discipline is the same. Models: Multi-LLM via the kit (e.g. Claude on Bedrock, Gemini, Azure OpenAI), selected per use case for quality, latency and cost. Interfaces: Tools and Model Context Protocol (MCP) for integration; standards-based APIs and secure auth for client systems. What you'll do • Conceptualize fast: embed with stakeholders, frame a business process as an agentic solution, and stand up a working agent prototype in days, not weeks. • Build Business Transformation Agents: design and ship single-agent and multi-agent systems in Python using ADKs that automate and transform real client workflows, with measurable ROI. • Own efficiency as the scorecard: drive delivery efficiency and operational efficiency ; shorter cycle times, less manual effort, higher accuracy, lower cost-to-serve. • Engineer the agent core: apply prompt engineering, context engineering, prompt caching, RAG / context-graph retrieval, memory, tool / function calling, MCP integration and multi-agent orchestration. • Integrate to standards: connect agents into client ecosystems through proven integration patterns, standards-based APIs and secure authentication. • Make reusability and predictability the default: build reusable agent components, skills, tool libraries and templates; add guardrails so agent behaviour is predictable, safe and repeatable. • Prototype and iterate quickly: use the kit's scaffolding to prototype, then harden to production-grade, well-tested Python. • Run eval-driven development: build evaluation harnesses and test suites that measure agent correctness, safety and regression before anything ships. • Own AgentOps / DevSecOps: CI/CD for agents, versioning, observability and telemetry, shift-left security, and Responsible AI governance baked in from day one. • Run a continuous, adaptable feedback loop: feed production telemetry, evals and client feedback back into prompts, context and agent design. • Stay ahead of the curve: adopt evolving agent frameworks and patterns quickly, and bring field learnings back to the practice. • Lead and mentor: set technical direction for a lean team of 3 agent engineers, raise the engineering bar, and grow the pod's agentic capability. What you'll bring (must-have) • Strong Python engineering ; idiomatic, typed, tested and packaged code; on a foundation of solid software engineering principles (design, version control, architecture). • Hands-on agent building with at least one agent development kit (Google ADK, LangGraph, CrewAI, OpenAI Agents SDK, AWS Bedrock AgentCore or Microsoft Agent Framework / Semantic Kernel). • Solid command of agent engineering: prompt

Key Responsibilities

1. Architect end-to-end AI/ML solutions using Python, TensorFlow, PyTorch, and scikit-learn, ensuring robust model development and deployment frameworks.
2. Design scalable data pipelines and real-time processing systems utilizing Apache Spark, Kafka, and PostgreSQL to support machine learning workflows.
3. Guide the team in implementing advanced ML models, including deep learning, NLP, and time series forecasting, using tools such as XGBoost, LightGBM, and Spark MLlib.
4. Oversee integration of data engineering platforms like Apache Airflow, DataBricks, and RabbitMQ to optimize data ingestion, transformation, and orchestration for AI/ML projects.
5. Ensure technical excellence by advocating best practices in model validation, performance optimization, and reproducibility across Python, R, and SQL-based environments.
6. Collaborate with stakeholders to gather requirements, translate business needs into technical specifications, and deliver tailored AI/ML solutions that meet quality and compliance standards.
7. Mentor and coach team members in advanced AI/ML concepts, fostering continuous learning and adoption of emerging technologies within the skill cluster.
8. Architect and implement RESTful API integrations to enable seamless communication between AI/ML components and external systems, ensuring scalable, secure, and efficient data exchange across diverse enterprise environments.

Skill Requirements

1. Expert Proficiency In Ai/Ml Model Development, Including Classical Machine Learning, Deep Learning, Nlp, And Time Series Forecasting.
2. Excellent Knowledge Of Python, R, Sql, And Bash For Data Analysis, Modeling, And Automation.
3. Expertlevel Experience With Tensorflow, Pytorch, Scikitlearn, Pandas, Numpy, Xgboost, Lightgbm, And Spark Mllib For Building And Deploying Models.
4. Advanced Proficiency In Designing And Managing Data Pipelines Using Apache Spark, Kafka, Airflow, Databricks, And Rabbitmq.
5. Excellent Understanding Of Relational Databases Such As Postgresql And Mysql For Data Storage And Retrieval.
6. Strong Ability To Translate Business Requirements Into Technical Solutions And Deliver Highcomplexity Ai/Ml Architectures.

Other Requirements

1. Optional But Valuable:
2. Google Professional Machine Learning Engineer
3. - Aws Certified Machine Learning � Specialty
4. - Optional: Databricks Certified Machine Learning Professiona