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We are looking for a Software Developer for one of our clients.
Job Responsibility:
Build Agentic Workflow Software (Core): Implement agentic workflows that execute multi-step domain tasks with human-in-the-loop checkpoints (review/approve/iterate) and clear auditability
Develop modular, extensible components (agents, tools, planners, evaluators) so workflows can be reused and expanded across use cases
Translate SME requirements into software artifacts: workflow definitions, task decomposition, tool contracts, state management, and error handling
Integrate with Data & Domain Ecosystems: Build robust integrations to enterprise data foundations (e.g., OSDU/ADME) for reading inputs and writing outputs with metadata/provenance
Implement connectors to domain tooling and services (e.g., subsurface/petrotechnical apps and workflows), supporting orchestration across systems
Collaborate with data engineers/data scientists to operationalize pipelines that support workflow execution and repeatability
Contribute to multi-agent workflow frameworks where LLMs assist with reasoning/planning and tools execute deterministic steps
Apply best practices for reliability (guardrails, evaluation, regression tests, and monitoring) in GenAI systems
Engineering Excellence & Operational Readiness: Write clean, testable Python code with CI/CD, code review, and documentation discipline
Add observability: logging, metrics, traces, structured events, and workflow run histories (supporting auditability)
Engineer workflows for production standards (security, performance, resiliency), with a bias toward maintainable systems (not “one-off demos”)
Requirements:
Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience
5+ years of professional software development experience
Demonstrated project delivery in Python (portfolio, GitHub, internal repos, or verifiable shipped work)
Strong Python development skills, demonstrated through delivered projects in production or near production environments (APIs/services, data workflows, automation, or platforms)
Experience building software with clear engineering fundamentals: modular design, testing, version control, packaging, performance considerations
Ability to collaborate effectively with domain SMEs and data scientists, can translate ambiguous domain goals into concrete, testable software
Experience working with data-centric systems (structured/unstructured data handling, ETL/ELT concepts, data quality, metadata)
Familiarity with modern development workflows, specifically with Azure DevOps (ADO) for Agile work management (Azure Boards), source control (Azure Repos), and/or CI/CD pipelines (Azure Pipelines)
Nice to have:
Familiarity with GenAI concepts and building blocks: LLM APIs, RAG pipelines, embeddings/vector databases, prompt design, and evaluation
Experience with agent frameworks and orchestration patterns (e.g., LangGraph / Semantic Kernel / similar), and tool-calling architectures
Exposure to “agentic workflow” patterns (goal-oriented loops, plan/act/observe/revise, human-in-loop gating)
Experience deploying containerized services (Docker/Kubernetes) and building runtime-ready APIs
Understanding of petrotechnical workflows and data types (subsurface interpretation, logs, reservoir/simulation workflows)
Builder mindset: ships working software, iterates fast, improves reliability over time
Strong written and verbal communication: can explain tradeoffs to SMEs and engineers
Pragmatic problem solver: balances GenAI “flexibility” with engineering rigor and validation
High ownership and integrity around data handling, access control, and compliance expectations in controlled environments