AI Engineer

Role Overview

As AI becomes a core enabler of enterprise transformation, we are seeking an AI Engineer who can design, train, fine-tune, and deploy Generative AI solutions while working closely with business stakeholders to define high-impact use cases. The role will be based in Pune, India with close collaboration across delivery, product, and business development teams.

The AI Engineer will be responsible for building and operationalizing AI capabilities across EIRIS's platform and client engagements. This role blends hands- on model development, prompt and pipeline engineering, MLOps, and business-facing solution design.

The ideal candidate combines deep technical expertise in LLMs, GenAI and Agentic frameworks with strong business understanding, enabling them to translate real-world problems into scalable GenAI solutions with measurable impact.

Key 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.

Experience

• 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, etc.).
• 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.

Key Skills and Attributes

• GenAI Expertise: Deep understanding of LLMs, RAG, agents, and multimodal AI.
• Business Orientation: Ability to define GenAI use cases tied to measurable business outcomes.
• Problem Solving: Translates ambiguity into structured AI solutions.
• Engineering Mindset: Builds scalable, secure, and production-ready systems.
• Communication: Effectively explains AI concepts to non-technical stakeholders.
• Ownership: Takes end-to-end responsibility from idea to production.
• Adaptability: Thrives in a fast-paced, rapidly evolving AI landscape.
• Ethical Awareness: Strong focus on responsible AI and governance.