Based onsite in Palo Alto, this role offers a base salary range of USD 128,500 to 171,500 per year, plus a comprehensive benefits package and an annual discretionary bonus. You will contribute to GenAI and agentic AI capabilities that power Coro's SaaS and data tools for B2B Commercial Excellence, guiding projects from proofs of concept to production deployments.
Benefits and workplace culture
- Bain pays 100% of individual employee premiums for medical, dental and vision coverage
- Generous paid time off including parental leave, sick leave and paid holidays
- Fully vested 401(k) company contribution
- 4.5% 401(k) company contribution, which increases after 3 years of service and is 100% vested upon start date
- Paid Life and Long-Term Disability insurance
- Annual fitness reimbursements
- Annual discretionary performance bonus
About the role and impact
You will join the Coro AI engineering team focused on GenAI and agentic capabilities that empower Bain's B2B Commercial Excellence tools. Your work spans from building AI powered tools and GenAI applications to integrating enterprise solutions, with an emphasis on reliability, security, and business value. The role blends hands-on development with direct collaboration in a client-facing consulting environment, translating client needs into practical technical plans and delivering end-to-end ML solutions.
Responsibilities
- Build AI powered tools and products that drive real business outcomes
- Design and develop GenAI applications such as copilots, workflow automation, and decision support for commercial teams using modern LLM stacks
- Implement agentic workflows with value, reliability, safety, and clear failure modes in mind
- Design and build advanced search, retrieval, and knowledge pipelines across diverse data structures and stores, including hybrid search, vector stores, graph databases, knowledge graphs, and traditional data platforms
- Cover indexing strategies, metadata design, relevance tuning, freshness, caching, access controls, and source attribution
- Develop robust agent capabilities including context engineering, memory and state management, orchestration, routing, and tool integration patterns
- Integrate solutions into enterprise environments and workflows via APIs, data systems, and collaboration tools while balancing quality, latency, cost, privacy, and adoption
- Translate ambiguous client needs into clear technical requirements, tradeoffs, and delivery plans
- Build and apply data science and machine learning capabilities; deliver ML solutions end-to-end including data prep, feature engineering, model selection, training, validation, and performance analysis
- Choose methods suitable for the problem, spanning classical ML and deep learning across sequence, text, and image models where relevant
- Create reproducible training and evaluation pipelines with versioning, experiment tracking, robust validation, and documentation
- Demonstrate fluency with modern deep learning concepts, including transformer basics and distinctions between pre-training, instruction tuning, and preference optimization
- Write clean, testable, maintainable code and ship AI services through the full SDLC: build, test, deploy, monitor and iterate
- Implement MLOps and GenAIOps practices: CI/CD, reproducibility, environment parity, model/prompt/agent versioning, and operational readiness
- Build evaluation and observability for GenAI and agentic systems: tracing, instrumentation, regression test suites, automated scoring where appropriate, and prompt/policy iteration
- Design for secure enterprise deployment with access controls, auditable data handling for sensitive and PII data, and responsible AI guardrails
- Build reusable components and accelerators that scale across client contexts
- Thrive in a client-facing consulting environment; communicate clearly with technical and non-technical stakeholders; lead working sessions and prepare concise technical documentation
- Collaborate with Bain consultants to prioritize critical technical decisions that unlock business value and support proposal shaping and scoping
Requirements
- Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience
- 3-5+ years of professional AI / ML engineering experience with strong backend fundamentals
- Strong proficiency in Python and experience building APIs / services (REST or gRPC) and integrating with enterprise systems
- Hands-on experience building LLM powered applications with attention to latency, cost, reliability and security
- Experience building advanced retrieval / search systems (hybrid retrieval, vector search, reranking) and working across data stores (vector, graph, relational/document/search)
- Experience implementing agentic patterns (context management, tool integration, orchestration, memory/state handling) with modern frameworks (LangGraph, OpenAI Agents SDK, Pydantic AI) or custom agent loops
- Experience creating reusable skills, tools, and services for agent use with schema validation (Pydantic) to enforce reliable data contracts
- Strong engineering practices: testing, code review, version control, CI/CD, and performance profiling
- Experience deploying and operating services on AWS, GCP, and/or Azure; environment management, reliability, observability, scaling
- Experience with Docker and Kubernetes and operating services in production (debugging, performance, resilience)
- Proven ability to implement security, privacy, and governance requirements for AI systems (authentication/authorization, access controls, PII handling, enterprise risk controls)
- Experience training, validating, and testing ML models; solid understanding of overfitting, generalization, and evaluation methodology
- Practical experience with feature engineering and data preprocessing for real-world datasets
- Familiarity with a broad set of ML algorithms (classical ML and deep learning) and ability to choose methods that fit business and data constraints
- Familiarity with deep learning frameworks (PyTorch or TensorFlow) and ML lifecycle tooling (experiment tracking, model registry, feature store concepts)
- Proven ability to operate in ambiguity, manage priorities, and deliver outcomes independently or with a team
- Excellent interpersonal and communication skills; able to explain technical decisions, tradeoffs, and results to diverse audiences
- Strong stakeholder management skills; comfortable working directly with clients
Technologies
- Python, REST, gRPC, LangGraph, OpenAI Agents SDK, Pydantic AI
- PyTorch, TensorFlow
- AWS, GCP, Azure
- Docker, Kubernetes
- MCP
Compensation and location
Location: Palo Alto, CA (onsite)
Salary range: USD 128,500 – 171,500 per year
Compensation includes base salary, an annual discretionary performance bonus, and a 401(k) program with employer contributions. California local guidance notes the good-faith annualized salary range for this role as $128,500–$171,500, with final placement based on experience and qualifications.
