About This Role

5 to 9 years of experience
Skills: Python, C/C++, Go, Java, OpenAI, Gemini, Llama, Qwen, Claude

AI Engineer

Role Overview

In this role, you will be responsible for launching and implementing GenAI agentic solutions aimed at reducing the risk and cost of managing large-scale production environments with varying complexities. You will address production runtime challenges by developing agentic AI solutions that can diagnose, reason, and take action in production environments — improving productivity and resolving production support issues.

What You'll Do

Build Agentic AI Systems

- Design and implement tool-calling agents that combine retrieval, structured reasoning, and secure action execution (function calling, change orchestration, policy enforcement) following the MCP protocol
- Engineer robust guardrails for safety, compliance, and least-privilege access

Productionize LLMs

- Build an evaluation framework for open-source and foundational LLMs
- Implement retrieval pipelines, prompt synthesis, response validation, and self-correction loops tailored to production operations

Integrate with Runtime Ecosystems

- Connect agents to observability, incident management, and deployment systems
- Enable automated diagnostics, runbook execution, remediation, and post-incident summarization with full traceability

Collaborate Directly with Users

- Partner with production engineers and application teams to translate production pain points into agentic AI roadmaps
- Define objective functions linked to reliability, risk reduction, and cost
- Deliver auditable, business-aligned outcomes

Safety, Reliability, and Governance

- Build validator models, adversarial prompts, and policy checks into the stack
- Enforce deterministic fallbacks, circuit breakers, and rollback strategies
- Instrument continuous evaluations for usefulness, correctness, and risk

Scale and Performance

- Optimize cost and latency via prompt engineering, context management, caching, model routing, and distillation
- Leverage batching, streaming,



and parallel tool-calls to meet stringent SLOs under real-world load

Build a RAG Pipeline

- Curate domain knowledge and build a data-quality validation framework
- Establish feedback loops and a milestone framework to maintain knowledge freshness

Raise the Bar

- Drive design reviews, experiment rigor, and high-quality engineering practices
- Mentor peers on agent architectures, evaluation methodologies, and safe deployment patterns

Role Requirements

Essential Skills

- Software Development: 5+ years of software development in one or more languages (Python, C/C++, Go, Java); solid hands-on experience building and maintaining large-scale Python applications preferred

- ML Systems: 3+ years designing, architecting, testing, and launching production ML systems, including model deployment/serving, evaluation and monitoring, data processing pipelines, and model fine-tuning workflows

- LLM Experience: Practical experience with Large Language Models (LLMs) — API integration, prompt engineering, fine-tuning/adaptation, and building applications using RAG and tool-using agents (vector retrieval, function calling, secure tool execution)

- Model Knowledge: Understanding of different LLMs, both commercial and open source, and their capabilities (e.g., OpenAI, Gemini, Llama, Qwen, Claude)

- Foundational Knowledge: Solid grasp of applied statistics, core ML concepts, algorithms, and data structures to deliver productive and reliable solutions

- Core Competencies: Strong analytical problem-solving, ownership, and urgency; ability to communicate complex ideas simply and collaborate effectively across global teams with a focus on measurable business impact

- Preferred: Proficiency building and operating on cloud infrastructure (ideally AWS), including containerized services (ECS/EKS), serverless (Lambda), data services (S3, DynamoDB, Redshift), orchestration (Step Functions), model serving (SageMaker), and infra-as-code (Terraform/CloudFormation)

Minimum Experience

- 5+ years of software development experience, including 3+ years designing and launching production ML systems

📌 AI Engineer (Bengaluru)
🏢 Bounteous
📍 Bengaluru