Sr. AI Engineer, Enterprise Applied AI

About Us

Client is on a mission to keep the world adventurous forever. This goes for the emissions-free Electric Adventure Vehicles we build, and the curious, courageous souls we seek to attract.

As a company, we constantly challenge what’s possible, never simply accepting what has always been done. We reframe old problems, seek new solutions and operate comfortably in areas that are unknown. Our backgrounds are diverse, but our team shares a love of the outdoors and a desire to protect it for future generations.

Role Summary

Join the Enterprise Applied AI team to build and ship production AI experiences and platforms that help internal teams work smarter and faster. As an AI Engineer, you will contribute across the stack, from data pipelines and retrieval to prompt/agent logic, evaluation/guardrails, and serving. You will collaborate closely with partners across Operations, Product, Sales, Customer Service, Finance, HR, and other internal teams to understand needs and deliver practical solutions that create tangible business value.

You will develop responsibly, partnering with governance stakeholders on privacy, security, compliance, and safety.

Responsibilities

- Build features and services across the AI stack: orchestration, retrieval/grounding, prompt/agent logic,
- evaluation/guardrails, serving, and observability.
- Implement robust data processing and integration pipelines to enable high-quality AI applications and analytics.
- Contribute to design docs, code reviews, testing, and operational playbooks to ensure reliability, maintainability,
- and resilience.
- Partner with product and business stakeholders to define requirements, iterate quickly, and measure outcomes
- using transparent success metrics.




- Instrument telemetry and evaluation to monitor quality, safety, latency, and cost; improve performance
- based on data.
- Follow responsible AI practices for security, privacy, compliance, and safety in collaboration with governance
- teams.
- Participate in on-call and incident response rotations as appropriate; drive post-incident improvements.
- Share learnings via demos and documentation; contribute to AI literacy and enablement across the org.
- Qualifications
- BS/MS in Computer Science or a related field, or equivalent experience.
- Practical software engineering experience building backend services, APIs, or data-intensive applications;
- strong foundations in algorithms, data structures, and systems.
- Exposure to or hands-on experience with LLM application concepts such as retrieval, grounding, prompt/agent
- design, function/tool use, evaluation, safety/guardrails, and cost/latency optimization.
- Proficiency with modern software delivery practices (version control, CI/CD, testing, observability); familiarity
- with cloud-native services and containerization.
- Ability to collaborate with product and business partners; strong written and verbal communication skills.
- Bias to ship, learn, and iterate; comfortable working in fast-evolving technology areas with incomplete
- information.
- For Senior level:



demonstrated ownership of services or platform components, end-to-end delivery of cross-service
- initiatives, and contributions to reliability/SLOs and operational excellence.

This is the summary

Senior AI Engineer (Applied AI) — Technical Competencies.
1. Agentic Workflows & Memory Systems

Stateful Orchestration: Building and debugging production-grade, cyclic multi-agent workflows and state machines. Context & Memory Engineering: Implementing multi-layered memory architectures including short-term memory for active turn execution, long-term memory for cross-session state persistence, and episodic summarization to manage token context windows.

Tool Call Management: Designing dependable function-calling patterns equipped with automated retry logic and self-correction handlers.

1. Data & Retrieval Infrastructure

Vector & Relational Storage: Managing relational metadata schemas and executing optimized semantic vector similarity searches inside a combined relational database layer. Document Persistence: Utilizing document-store databases to store unstructured execution payloads, dynamic agent states, and raw chat logs.

Hybrid RAG Pipelines: Combining relational/exact-match queries with vector-space searches for high-precision retrieval.

1. Production Reliability & Performance

Granular Tracing: Instrumenting end-to-end tracing to monitor agent execution steps, debug non-deterministic loops, and track token costs. Automated Evals: Creating programmatic evaluation testing and scoring frameworks to benchmark agent accuracy before production deployment.

Core Backend Development: Writing clean, concurrent, asynchronous Python code to handle high-throughput foundation model APIs.

📌 Sr. AI Engineer (India)
🏢 Pentangle Tech Services | P5 Group
📍 India