Sr AI Engineer

Job Description

About the role:

We’re hiring a Senior AI Engineer to lead the development and deployment of cutting-edge AI

systems, with a focus on large language models (LLMs), real-time conversational agents,

multimodal intelligence, and multilingual capabilities. This role bridges deep ML expertise, system design, and hands-on implementation — ideal for someone excited to turn AI innovation into real world product impact. You’ll work across open-source and commercial models, cloud platforms, and global user contexts to shape the future of AI-driven user experiences.

*** This is a hybrid position - working 3 days a week from our Bangalore office. ***

Key Responsibilities:

LLM & Multimodal AI Development:

Design, fine-tune, and evaluate LLMs (e.g., LLaMA, Mistral) using modern training workflows (LoRA, QLoRA, DPO, SFT).

Build and integrate AI systems that process and generate across text, audio, images, and structured data.

Engineer robust prompts and control flows for multilingual, compliance-sensitive, and regulated domains.

Develop and maintain retrieval-augmented generation (RAG) pipelines and vector databases to ground AI outputs in enterprise data and conversational history.

Production-Grade Delivery & Cloud Deployment:

Deploy scalable, secure ML systems on AWS, Azure, and Google Cloud.

Translate prototypes into reliable, cost-optimized production systems.

Run A/B tests and integrate feedback loops into model refinement cycles.

Evaluation, Safety & Compliance:

Develop structured evaluation frameworks for accuracy, latency, safety, and compliance.

Calibrate models for predictable, safe, and compliant behavior in production environments.

Collaboration, Mentorship & Best Practices:

Partner with cross-functional teams to align AI solutions with business needs.

Mentor junior engineers and champion MLOps/LLMOps best practices.

Required Skills & Qualifications:

5+ years of experience in ML/AI development and deployment, including 1–2 years working with LLMs or Generative AI.

Hands-on experience with LLM architectures, training workflows, and multimodal AI systems.

Proficient in prompt engineering for multilingual and regulated domains.

Strong Python skills; familiarity with Hugging Face, LangChain or LlamaIndex, vector databases, and inference tuning.

Experience working with conversational data, annotation pipelines, and outcome-linked training sets.

Deep knowledge of ML tooling across AWS, Azure, and GCP.

MLOps expertise: containerization, orchestration, CI/CD, distributed inference, and real-time ML systems.

Bachelor’s or master’s degree in computer science, Machine Learning, or a related field.

Ideal Candidate Traits:

Designs and builds end-to-end systems (model, prompt, deployment).

Focused on real-world performance and measurable impact.

Adapts quickly to evolving GenAI tools and frameworks.

Communicates technical concepts clearly to both technical and non-technical stakeholders.

Enjoys building from scratch and thrives in ambiguity.