Middle/Senior Forward Deployed Engineer (Python, AI)

Role overview A forward-deployed engineering role pairing hands-on AI software development with client-facing delivery inside enterprise environments. Engineers embed onsite with operational teams and executive sponsors, translate ambiguous business problems into prioritized opportunities, and ship production-grade AI agents, data pipelines, and applications. Senior-level candidates additionally lead pod execution, own engagement outcomes, and mentor other forward-deployed engineers.

Responsibilities - Lead discovery sessions with client stakeholders to map business systems, data, operating models, and underlying challenges. - Translate ambiguous business problems into quantified opportunity maps and deliver working, data-grounded prototypes early in each engagement. - Design, develop, debug, and deploy production-grade AI agents, integrations, data pipelines, and applications directly within client systems. - Define evaluation frameworks, quality thresholds, regression gates, monitoring strategies, and human-in-the-loop controls for AI and agentic workflows. - Scope, sequence, and coordinate delivery across engineers, architects, and specialist contributors, making explicit trade-offs between scope, speed, quality, and value. - Convert successful deployments into reusable reference architectures, agent templates, evaluation frameworks, and delivery playbooks, while training client teams to operate and extend the work.

Requirements - Mid-level hands-on technical experience with client-facing capacity; senior track requires delivery leadership across multidisciplinary teams or pods. - Proven record of delivering production software used by real users and business teams, including LLM-backed or agentic systems. - Advanced proficiency in Python, plus TypeScript or JavaScript and SQL. - Strong knowledge of prompt and context engineering, agent architecture, tool and function calling, retrieval-augmented generation, and frameworks such as the OpenAI Agents SDK or Claude SDKs. - Experience with evaluation harnesses, drift monitoring, observability, human-in-the-loop control design, data modeling, ETL/ELT pipelines, vector stores, and embedding pipelines. - Cloud deployment experience on AWS, Azure, or GCP, plus familiarity with Docker, Kubernetes, CI/CD, infrastructure as code, REST or GraphQL APIs, and SSO. - Willingness to travel approximately 25–50% and work onsite in client environments, with fluent English communication.

Nice to have - Java, C#, or C++ within legacy enterprise environments. - Node.js, React, or Next.js for client-facing applications. - Fine-tuning fundamentals and multi-agent orchestration experience. - TDD or BDD practices, large-scale data platforms such as Spark, Databricks, or Snowflake, and on-premises or sovereign deployment familiarity. - Experience with enterprise systems such as Jira, Azure DevOps, ServiceNow, Salesforce, or SAP, and GitHub or GitLab-based delivery workflows.

Benefits and work setup - International project exposure with in-office, hybrid, or remote flexibility depending on location and contract type. - Medical healthcare, recognition program, ongoing learning and reimbursement, well-being program, team events, sports compensation, referral bonuses, and top-tier equipment provision.