What are the responsibilities and job description for the AI engineer position at Geethanjali College of Engineering and Technology?
AI Engineer
anywhere in US
work in EST time zone
• 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). 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.
• 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.
• Raise the bar: Drive design reviews, experiment rigor, and high-quality engineering
practices; mentor peers on agent architectures, evaluation methodologies, and saf
deployment patterns
ESSENTIAL SKILLS
1. 5 years of software development in one or more languages strong hands-on
experience building and maintaining large-scale Python applications preferred.
2. 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).
3. Understanding of different LLMs, both commercial and open source, and their
capabilities (e.g., OpenAI, Gemini, Llama, Qwen, Claude).
4. Solid grasp of applied statistics, core ML concepts, algorithms, and data structures to
deliver efficient and reliable solutions.
5. 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.
anywhere in US
work in EST time zone
• 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). 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.
• 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.
• Raise the bar: Drive design reviews, experiment rigor, and high-quality engineering
practices; mentor peers on agent architectures, evaluation methodologies, and saf
deployment patterns
ESSENTIAL SKILLS
1. 5 years of software development in one or more languages strong hands-on
experience building and maintaining large-scale Python applications preferred.
2. 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).
3. Understanding of different LLMs, both commercial and open source, and their
capabilities (e.g., OpenAI, Gemini, Llama, Qwen, Claude).
4. Solid grasp of applied statistics, core ML concepts, algorithms, and data structures to
deliver efficient and reliable solutions.
5. 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.