Sr AI/ML Solutions Architect - PostgreSQL

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 scalability, performance, and security across cloud and on-premise environments.
2. Design and implement distributed data processing pipelines with Apache Spark and Kafka, optimizing for real-time analytics and robust data ingestion.
3. Develop and operationalize machine learning models for NLP, deep learning, and time series forecasting using Spark Mllib, XGBoost, and LightGBM, ensuring seamless integration with PostgreSQL and DataBricks platforms.
4. Establish best practices for model deployment and monitoring using Apache Airflow and Bash scripting, enabling automated workflows and continuous delivery.
5. Mentor and guide technical teams in advanced ML model development, fostering skill growth in Python, R, and SQL, and mitigating delivery risks through knowledge transfer.
6. Collaborate with stakeholders to gather requirements, define technology strategy, and align AI/ML architectures with evolving business needs and industry standards.
7. 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.
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 Ml, Deep Learning, Nlp, And Time Series Forecasting.
2. Excellent Skills In Python, R, Sql, And Bash For Data Manipulation, Model Training, And Automation.
3. Advanced Expertise In Tensorflow, Pytorch, Scikitlearn, Pandas, Numpy, Xgboost, And Lightgbm For Building And Deploying Machine Learning Models.
4. Excellent Knowledge Of Distributed Computing And Data Engineering With Apache Spark, Spark Mllib, Apache Kafka, Rabbitmq, And Databricks.
5. Solid Understanding Of Relational Databases, Especially Postgresql And Mysql, For Data Storage And Retrieval In Ml Workflows.
6. Expert Ability To Design Secure, Scalable Architectures And Implement Best Practices For Ai/Ml Solution Delivery.

Other Requirements

1. Optional But Valuable:
2. Certifications Such As Tensorflow Developer Certificate
3. - Aws Certified Machine Learning � Specialt