Responsibilities:

Design, train, fine-tune, and evaluate Generative AI models (LLMs, multimodal models) for enterprise use cases.
Develop and optimize prompt engineering, RAG pipelines, agents, and fine-tuning workflows.
Design, develop, and optimize a multi‑agent agentic framework that enables autonomous, domain‑aware collaboration across specialized agents.
Work with open-source and commercial LLMs (OpenAI, Anthropic, LLaMA, Mistral, etc.).
Implement guardrails, safety mechanisms, and hallucination mitigation techniques.
Partner with business, consulting, and product teams to identify, evaluate, and prioritize GenAI use cases.
Translate business problems into clear GenAI solution architectures and success metrics.
Create solution blueprints, prototypes, and POCs to demonstrate business value.
Design and manage data pipelines for AI training, fine-tuning, and inference.
Build scalable, secure, and cost-efficient GenAI systems for production environments.
Collaborate with engineering teams on deployment, monitoring, and retraining strategies.
Monitor model performance, latency, cost, and drift in production.
Ensure responsible, ethical, and compliant use of Generative AI.
Implement explainability, auditability, and traceability mechanisms where required.
Address data privacy, IP protection, and regulatory constraints (e.g., GDPR).
Define and enforce AI best practices, standards, and usage guidelines.

Good to have:

Prior 5–9 years of experience in data science, ML engineering, or AI development, with 2+ years focused on Generative AI.
Strong hands-on experience with LLMs, transformers, embeddings, and vector databases.
Proficiency in Python and GenAI frameworks (LangChain, LlamaIndex, Haystack, Hugging Face).
Experience with fine-tuning techniques (LoRA, PEFT, instruction tuning).
Experience designing multi‑agent systems, including orchestration, coordination, and distributed reasoning.
Familiarity with MLOps tools, CI/CD pipelines, and model monitoring.
Familiarity with 3D deep learning and large scale point cloud processing is a plus.
Experience working in fast paced startup environment (preferred).
Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or a related field.

Responsibilities:

Design, train, fine-tune, and evaluate Generative AI models (LLMs, multimodal models) for enterprise use cases.
Develop and optimize prompt engineering, RAG pipelines, agents, and fine-tuning workflows.
Design, develop, and optimize a multi‑agent agentic framework that enables autonomous, domain‑aware collaboration across specialized agents.
Work with open-source and commercial LLMs (OpenAI, Anthropic, LLaMA, Mistral, etc.).
Implement guardrails, safety mechanisms, and hallucination mitigation techniques.
Partner with business, consulting, and product teams to identify, evaluate, and prioritize GenAI use cases.
Translate business problems into clear GenAI solution architectures and success metrics.
Create solution blueprints, prototypes, and POCs to demonstrate business value.
Design and manage data pipelines for AI training, fine-tuning, and inference.
Build scalable, secure, and cost-efficient GenAI systems for production environments.
Collaborate with engineering teams on deployment, monitoring, and retraining strategies.
Monitor model performance, latency, cost, and drift in production.
Ensure responsible, ethical, and compliant use of Generative AI.
Implement explainability, auditability, and traceability mechanisms where required.
Address data privacy, IP protection, and regulatory constraints (e.g., GDPR).
Define and enforce AI best practices, standards, and usage guidelines.

Good to have:

Prior 5–9 years of experience in data science, ML engineering, or AI development, with 2+ years focused on Generative AI.
Strong hands-on experience with LLMs, transformers, embeddings, and vector databases.
Proficiency in Python and GenAI frameworks (LangChain, LlamaIndex, Haystack, Hugging Face).
Experience with fine-tuning techniques (LoRA, PEFT, instruction tuning).
Experience designing multi‑agent systems, including orchestration, coordination, and distributed reasoning.
Familiarity with MLOps tools, CI/CD pipelines, and model monitoring.
Familiarity with 3D deep learning and large scale point cloud processing is a plus.
Experience working in fast paced startup environment (preferred).
Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or a related field.

Senior AI Engineer (Startup/ Energy & Utility/ 8+ years)

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