Senior AI Engineer

Essential functions

• Design, build, and maintain the platform’s core model runtime, managing backends, per-stage model assignment, prompt caching, and context window token budgeting.

• Implement safe, cost-effective, diff-only repository scanning using tree-sitter call graphs, resilient fingerprinting, and concurrency leases.

• Build dynamic, permission-aware RAG/retrieval capabilities over enterprise knowledge bases (Confluence, ADRs, design docs) and deliver versioned detection lenses and rule packs.

• Develop robust integrations and connectors across GitHub, GitLab, Jira, and CI/CD pipelines, automating draft PR generation, issue tracking, and short-lived credential management.

• Establish system observability (tracing per stage, token/cost tracking, SOC logging) and enforce strict release hygiene, including containerization, lockfiles, and signed builds.

• Wire external security scanners (SBOM, SCA, secrets, KEV, EPSS) into unified harness findings and build reviewer feedback loops to track precision and GRC/ASPM exports.

• Execute secure sandboxing, runtime checkpointing, and throwaway worktree mitigations to guarantee safety during automated remediation loops.

Qualifications

• 5+ years of commercial software, ML platform, or infrastructure engineering experience building enterprise-grade systems.

• Strong hands-on proficiency in Python, Go, or Rust, along with deep expertise in LLM infrastructure, model runtimes, prompt engineering, and RAG architectures.

• Hands-on experience with code parsing tools (e.g., Tree-sitter) for AST analysis, boundary chunking, and dependency graph extraction.

• Demonstrated capability in automating developer workflows across Git platforms (GitHub, GitLab), Jira, and CI/CD systems using ephemeral authentication.

• Strong expertise in release engineering and platform security, including containerization, signed builds, Infrastructure as Code, and secure-by-design practices.

• Solid background in system observability, implementing tracing, logging, and token/cost metrics tracking across complex platform pipelines.

Would be a plus

• Practical experience integrating external security scanners (SBOM, SCA, secrets) or vulnerability intelligence feeds (KEV, EPSS).

• Domain knowledge in payment processing, financial services, or highly regulated enterprise environments.

• Hands-on experience with GRC and ASPM tool ecosystems.

• Background in deterministic evaluation frameworks, baseline measurement, and regression testing for AI loops.

We offer

• Opportunity to work on bleeding-edge projects
• Work with a highly motivated and dedicated team
• Competitive salary
• Flexible schedule
• Benefits package - medical insurance, sports
• Corporate social events
• Professional development opportunities
• Well-equipped office

About us

Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Fusing technical vision with business acumen, we solve the most pressing technical challenges and enable positive business outcomes for enterprise companies undergoing business transformation. A key differentiator for Grid Dynamics is our 8 years of experience and leadership in enterprise AI, supported by profound expertise and ongoing investment in data, analytics, cloud & DevOps, application modernization and customer experience. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.