Top Tech Jobs & Startup Jobs in Boston, MA

Leads enterprise AI architecture for generative AI, agentic systems, traditional machine learning, and computer vision. Designs secure, scalable platforms; establishes MLOps and LLMOps standards; prototypes and writes production code; leads client solutioning and pre-sales; ensures regulatory compliance; mentors architects and engineers; and governs technical standards across strategic engagements.

Design, build, and optimize scalable infrastructure for GenAI/LLM workloads across multi-GPU environments. Perform GPU profiling and distributed training optimization, manage Slurm and Kubernetes/OpenShift clusters, enable NVIDIA GPU stack, create IaC templates (Terraform/Helm), and collaborate with data science and MLOps teams to deploy production GenAI pipelines.

Architect and implement enterprise MLOps and LLMOps platforms on Google Cloud. Own model lifecycle pipelines, CI/CD automation, deployment, monitoring, drift detection, lineage, governance, and observability. Build GKE-based ML platforms, conduct architecture reviews, troubleshoot complex systems, collaborate with engineering and client teams, and mentor technical staff. Travel up to 30% is required.

Architect and build production-grade multi-agent AI systems, agentic workflows, RAG solutions, tool libraries, memory systems, and evaluation frameworks. Deploy scalable, low-latency solutions across AWS, GCP, or Azure using MLOps, CI/CD, observability, and infrastructure automation. Integrate and optimize LLMs, implement monitoring and safety controls, establish performance metrics, and lead technical delivery while mentoring team members and communicating complex AI concepts to stakeholders.

Designs and productionizes hybrid knowledge graph solutions for healthcare and clinical data using LPG and RDF technologies. Builds FHIR transformation and ingestion pipelines, implements ontology mappings, entity linking, identifiers, provenance, validation, and graph queries with Cypher or SPARQL. Collaborates with ontology, terminology, FHIR, clinical, and data engineering teams, while contributing to architecture reviews, code reviews, testing, and engineering best practices.

Leads delivery of large-scale AWS and digital transformation programs, overseeing planning, execution, reporting, risk mitigation, quality, and customer outcomes. Manages multi-functional teams of business analysts and engineers, develops product and service roadmaps, drives continuous improvement, and resolves customer issues. The role also involves people management, recruiting, coaching, business development, stakeholder communication, and aligning program portfolios with organizational strategy.

Architect and implement enterprise-grade MLOps/LLMOps platforms on AWS and Kubernetes. Design ML/LLM pipelines for training, validation, deployment, monitoring, governance, CI/CD, and observability. Serve as technical authority, conduct architecture reviews, ensure security and compliance, and mentor engineering teams across projects.

Design and deliver enterprise-grade generative AI solutions on AWS using Bedrock, AgentCore, SageMaker, LLMs, RAG pipelines, vector databases, and agentic workflows. Build scalable APIs and integrations, optimize prompts and models, evaluate performance, and implement security and governance. Collaborate with application, data, and platform teams, troubleshoot production systems, define best practices, and mentor engineers while remaining hands-on.

Designs, builds, and productionizes healthcare knowledge graph solutions using LPG and RDF technologies. Develops FHIR data transformation and ingestion pipelines, ontology mappings, entity linking, provenance tracking, graph queries, and automated validation. Translates semantic specifications into scalable implementations while collaborating with ontology, terminology, clinical, FHIR, and data engineering teams. Participates in technical design, architecture reviews, code reviews, and engineering best practices.

Design and maintain Snowflake-based data platforms, cloud-native data lakes, and scalable ETL/ELT pipelines for asset-backed finance analytics. Orchestrate workflows with Dagster and AWS services, ensure data reconciliation and integrity, and support migration from legacy systems. Contribute to project planning, Jira backlog management, testing, cutover planning, and cross-functional go-live readiness.