Machine Learning Engineer

Machine Learning Engineer — Deccan AI

Location

Hyderabad, India (Hybrid)

Experience

4–6 Years

Function

Machine Learning Engineering

Employment Type

Full-time

About Deccan AI

Deccan AI is a fast-growing, venture-backed AI infrastructure company focused on training, evaluating, and improving next-generation AI systems. Headquartered in the Bay Area, with a growing India hub in Hyderabad, the company was founded by alumni of IIT Bombay, IIM Ahmedabad, and former Google leaders.

We work with some of the world's leading AI frontier labs and research organizations, including Google DeepMind, Snowflake, and other cutting-edge AI teams. Backed by Prosus Ventures, Deccan AI recently raised $25M in Series A funding and is entering a significant growth phase.

With a global network of over 1 million experts, advanced automation systems, and vertically integrated platforms, we deliver the high-quality data and evaluation infrastructure that state-of-the-art AI models depend on. As the AI infrastructure market rapidly expands, Deccan AI is building the systems powering the future of AI.

About the Role

We are looking for an experienced Machine Learning Engineer to help build and scale the infrastructure that transforms advanced AI research into reliable, production-grade systems.

You will operate at the intersection of machine learning research and engineering — developing evaluation frameworks, fine-tuning and benchmarking LLMs and agents, and building scalable automation systems that support large-scale human-in-the-loop AI workflows.

This role is designed for engineers who have experience building and deploying complex ML systems at scale and are excited to work on foundational problems shaping frontier AI development.

Key Responsibilities

• Design, develop, and own end-to-end ML pipelines across data curation, annotation, training, evaluation, and inference
• Translate research concepts into scalable engineering systems, including benchmarks, reward models, and evaluation datasets
• Fine-tune, evaluate, and stress-test LLMs, multimodal systems, and AI agents across coding and non-coding domains
• Build automation systems such as LLM-as-a-judge frameworks, synthetic data pipelines, and active learning workflows
• Optimize distributed training and large-scale inference systems for scalability, latency, reliability, and cost efficiency
• Collaborate closely with ML researchers, engineers, product teams, and client stakeholders to deliver production-grade AI systems
• Contribute to architecture reviews, engineering standards, system design decisions, and platform scalability initiatives

Required Qualifications

Experience

• 4–6 years of hands-on ML engineering experience in production environments
• Prior experience at leading technology companies, frontier AI labs, or high-growth AI startups
• Bachelor's, Master's, or PhD degree in Computer Science, Electrical Engineering, or related disciplines from top-tier institutions such as IITs, IIITs, BITS Pilani, IISc, top NITs, or equivalent international universities

Technical Skills

• Expert-level proficiency in Python with strong software engineering fundamentals
• Deep hands-on experience with PyTorch, TensorFlow, or JAX
• Strong expertise in:
• Distributed training frameworks such as DDP, FSDP, and DeepSpeed
• Efficient inference systems including vLLM, TensorRT, and Triton
• LLM fine-tuning approaches such as SFT, LoRA, and QLoRA
• RLHF, DPO, evaluation systems, or large-scale ML data pipelines
• Strong understanding of:
• Transformers and attention mechanisms
• Tokenization and sampling strategies
• Scaling laws and evaluation methodologies
• Distributed systems and ML infrastructure design
• Strong computer science fundamentals including algorithms, data structures, and system design

Working Style

• Demonstrated ability to independently own and execute complex technical systems
• Strong communication and collaboration skills
• Comfortable discussing technical concepts with researchers, engineering teams, and leadership stakeholders

Preferred Qualifications

• Experience building or evaluating agentic frameworks such as LangGraph, AutoGen, CrewAI, or ReAct systems
• Exposure to reinforcement learning workflows including RLHF, RLAIF, DPO, GRPO, or reward modeling
• Experience working on multimodal AI systems including vision-language, audio-language, or code-generation models
• Familiarity with MLOps and infrastructure tooling such as Kubernetes, Ray, Airflow, MLflow, Weights & Biases, Vertex AI, or SageMaker
• Publications at leading conferences such as NeurIPS, ICML, ICLR, ACL, EMNLP, or CVPR
• Meaningful open-source contributions or strong competitive ML/Kaggle experience
• Prior experience in AI data infrastructure or AI evaluation companies

Interview Process

Recruiter Discussion:

Assessment of background, experience, and motivation

Technical Project Deep Dive:

Detailed walkthrough of a complex ML project with focus on technical ownership and implementation depth

Technical Evaluation:

Coding assessment, ML systems design discussion, and practical implementation evaluation focused on LLMs and ML engineering

Culture & Team Fit:

Evaluation of communication, collaboration, ownership mindset, and alignment with Deccan AI's operating culture

What We Offer

• Opportunity to work directly with frontier AI labs on systems deployed into state-of-the-art AI models
• Challenging, high-impact engineering problems typically seen in leading global AI organizations
• High ownership, fast execution cycles, and low bureaucracy
• Competitive compensation and accelerated career growth
• A high-performance team comprising IIT/IIM alumni and engineers from Google, Meta, and leading AI organizations

Deccan AI is an equal opportunity employer. We evaluate candidates based on merit, technical excellence, and impact.