Overview In this role you will advance AI-enabled transformations for enterprise clients within Bain AIS, building GenAI and agentic AI applications that move from prototype to production. You will work in cross-functional teams with consultants, engineers, and client stakeholders to deliver measurable business impact through integrated AI solutions. You'll design scalable, reliable AI workflows, tackle data pipelines and evaluation, and ensure secure, governance-backed deployments. This is a hands-on, client-facing engineering leadership role focused on delivering real value at scale. Compensation / Benefitsbase salary plus discretionary performance bonus401(k) with employer contributioncomprehensive medical/dental/vision benefitsgenerous paid time off and holidayspar tial fitness reimbursementsfully vested 401(k) company contribution ResponsibilitiesDesign and develop GenAI applications (e.g., copilots, workflow automation, decision support) using modern LLM stacksImplement agentic workflows with tool use, multi-step execution, and human-in-the-loop controls emphasizing reliability and safetyBuild advanced search, retrieval, and knowledge pipelines across diverse data structures and storesCreate robust agent capabilities including context engineering, memory/state management, orchestration, routing, and tool integration patternsIntegrate AI solutions into enterprise environments and workflows (APIs, data systems, collaboration tools)Translate ambiguous client needs into clear technical requirements and delivery plansBuild ML solutions end-to-end (data prep, feature engineering, model selection, training, evaluation)Write clean, testable code and ship AI services through the full SDLC (build, test, deploy, monitor, iterate)Implement MLOps and GenAIOps practices (CI/CD, reproducibility, versioning, operational readiness)Design for secure enterprise deployment (access controls, PII handling, guardrails)Develop reusable components, accelerators, and connectors for scale across client contextsCommunicate with stakeholders, lead sessions, write technical documentationSupport proposal shaping, scoping, and delivery roadmaps Key requirements3-5+ years of AI/ML engineering experienceStrong Python backend engineering and API/service developmentHands-on experience building LLM-powered applications with latency, cost, reliability considerationsExperience with retrieval/search systems and multi-store data architecturesExperience implementing agentic patterns and memory/state managementStrong software engineering practices (testing, code review, CI/CD, performance profiling)Experience deploying services on AWS, GCP, and/or Azure; Docker and Kubernetes experienceSecurity, privacy, and governance practices for AI systems (PII handling, access controls)Knowledge of ML models, feature engineering, and evaluation methodologiesFamiliarity with deep learning frameworks (PyTorch/TensorFlow) and ML lifecycle toolingExcellent communication and stakeholder managementAbility to operate in ambiguity and manage prioritiesClient-facing collaboration and presentation skillsPythonREST/gRPC APIsLLM technologies and pipelines
Not specified in the original listing.
Not specified in the original listing.
