Mechanical Engineer jobs in United States

As a Technical Lead within the Digital team, you will partner with clients to navigate fast‑evolving business environments and accelerate their digital transformation journey.

This is a hands‑on leadership role ideal for someone passionate about Gen AI, Agentic AI, and modern engineering practices.

• Translate business goals into Gen AI and Agentic AI enabled architectures (e.g., RAG, autonomous agents).
• Own the full solution design lifecycle across application, data, integration, security, and observability layers.
• Lead technical discussions with clients, guide delivery teams, and provide strong solution governance.
• Drive hands‑on R&D initiatives, prototypes, and POCs that evolve into successful MVPs. Establish robust LLMOps capabilities including evaluation frameworks, observability, guardrails, and prompt/version management aligned with SLOs.
• Architect secure and scalable data flows (batch/stream); integrate vector search and define APIs/events for tools and agents.
• Evaluate platform and model options (managed vs OSS), optimizing for cost, performance, and risk.
• Ensure security and privacy‑by‑design compliance: Zero Trust, IAM/OIDC, secrets management, KMS, and data governance.
• Oversee delivery roadmaps, backlogs, multidisciplinary teams, design/code reviews, and technical standards.
• Engage stakeholders through discovery workshops, roadmap planning, estimates, and business value articulation.
• Mentor and upskill engineering teams; create reusable frameworks, blueprints, and reference architectures.

• Basic Qualifications: Bachelor’s degree or equivalent professional qualification.
• 12+ years of enterprise architecture experience with 2+ years designing production‑grade Gen AI solutions.
• Strong foundation in Data Science / Machine Learning concepts.
• Proven expertise across cloud (AWS, Azure, or GCP), containers/Kubernetes/serverless, and IaC (Terraform/CloudFormation).
• Hands‑on experience with at least one RAG/Agent stack: LangChain / LangGraph, DSPy, OpenAI or Anthropic tools, Databricks Agents, etc.
• Practical LLMOps experience with evaluation: LLM judges, task metrics.
• Observability: MLflow, OpenTelemetry Prompt/Version registries, CI/CD for AI workloads.
• Strong Data & Lakehouse expertise (Delta/Iceberg/Hudi), event streaming (Kafka/Kinesis/Pub/Sub), and vector search (Pinecone, pgvector, Milvus, Vespa, etc.).
• API engineering: REST, gRPC, GraphQL, event‑driven patterns and version management.
• Security standards: OAuth2/OIDC, JWT/mTLS, secrets governance, classification/lineage, safety guardrails.
• Excellent communication skills with ability to articulate ROI, risks, and architecture decisions to technical and non‑technical stakeholders.
• Proven experience in managing multiple initiatives and client expectations effectively.
• Strong leadership mindset with continuous learning and delivery excellence focus.

AWS/Azure/Google AI Certifications, AWS/Azure/Google Data Science Certifications, AWS/Azure/Google Solution Architect Certifications, TOGAF 9, other relevant industry‑standard architecture certifications.