Artificial Intelligence Engineer

Bounteous; Bangalore Rural; On-site; INR 3,500,000 - 6,000,000; Full time

Job summary

Bounteous is seeking an AI Engineer to lead the development of GenAI agentic solutions for large-scale production environments in India. You will design tool-calling agents, implement retrieval and reasoning, and integrate with observability and deployment systems to automate diagnostics and remediation.

The role requires 5+ years of software development and 3+ years delivering production ML systems, with strong knowledge of LLMs, RAG, and cloud infrastructure (AWS).

Qualifications

• 5+ years of software development in one or more languages (Python, C/C++, Go, Java).
• 3+ years designing, architecting, testing, and launching production ML systems, incl. model deployment/serving, monitoring, data pipelines.

Responsibilities

• Build agentic AI systems: design and implement tool-calling agents with retrieval, reasoning, and secure action execution.
• Productionize LLMs: build evaluation framework, implement retrieval pipelines, prompts, and self-correction for production ops.
• Integrate with runtime ecosystems: connect agents to observability, incident management, and deployment systems for automated diagnostics and runbook execution.
• Collaborate with users: translate production pain points into AI roadmaps; define objectives linked to reliability, risk reduction, and cost.
• Safety, reliability, governance: build validators, adversarial prompts, policy checks; ensure deterministic fallbacks and rollback strategies.
• Scale and performance: optimize cost/latency via prompt engineering, context management, batching; meet SLOs under load.
• Build a RAG pipeline: curate domain knowledge, validate data quality, establish feedback loops and knowledge freshness.

Skills

Python

C/C++

Go

Java

LLMs

RAG & tool-using agents

Cloud AWS

Tools

SageMaker

ECS/EKS

Lambda

S3

DynamoDB

Redshift

Terraform/CloudFormation

Job description

Role Name/ Team: PRX- AI Engineer

Minimum Years Experience: 5+Years

Job Description : 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 various production runtime challenges by developing agentic AI solutions that can diagnose, reason, and take actions in production environments to improve productivity and address issues related to production support.

What youll 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 MCP protocol. Engineer robust guardrails for safety, compliance, and least-privilege access.
• Productionize LLMs: Build 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 to 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; and 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; build data-quality validation framework; establish feedback loops and milestone framework maintain knowledge freshness.

ESSENTIAL SKILLS

1. 5+ years of software development in one or more languages (Python, C/C++, Go, Java); strong hands-on experience building and maintaining large-scale Python applications preferred.

2. 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.

3. 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).

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

5. Solid grasp of applied statistics, core ML concepts, algorithms, and data structures to deliver efficient and reliable solutions.

6. 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.

7. 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).