Accountabilities:

Design, develop, and deploy production-ready AI systems, including LLMs, RAG pipelines, agentic workflows, and machine learning/deep learning solutions.

Select appropriate models, architectures, and technologies while balancing accuracy, latency, cost, scalability, and complexity.

Train, fine-tune, and optimize machine learning models using frameworks such as PyTorch or Keras, including data preparation and model evaluation.

Research, implement, and benchmark emerging AI techniques before integrating them into production environments.

Develop APIs, inference pipelines, backend integrations, and supporting infrastructure for AI-powered applications.

Define and monitor performance metrics related to model quality, latency, cost, and reliability.

Evaluate, debug, and optimize AI systems after deployment, ensuring production readiness and identifying potential regressions before they affect users.

Collaborate with software engineers, backend developers, product teams, and engineering leadership to integrate AI capabilities into existing applications and workflows.

Contribute to technical discussions, architecture decisions, and engineering best practices.

Document system architecture, implementation strategies, technical decisions, and operational processes to support maintainability and scalability.

Review machine learning research papers, reproduce relevant findings, and translate promising research into practical engineering solutions.

Contribute to ongoing AI research, experimentation, and innovation initiatives.

Work effectively with globally distributed engineering and research teams while adapting to evolving technical and business priorities.

Requirements

3+ years of hands-on AI/ML engineering experience, including experience deploying at least one AI/ML system into production.

Strong understanding of natural language processing fundamentals, including tokenization, embeddings, classification, named entity recognition (NER), and information extraction.

Solid software engineering skills, including coding, Git, testing, debugging, API development, and system design.

Practical experience training machine learning models using PyTorch or Keras.

Understanding of generative model architectures, including Transformers and Variational Autoencoders (VAEs).

Production experience with LLM-based systems, including RAG, embeddings, vector databases, prompt engineering, and evaluation methodologies.

Experience developing APIs using FastAPI or similar frameworks.

Familiarity with Docker and at least one major cloud platform, such as AWS, GCP, or Azure.

Ability to read machine learning research papers, evaluate technical approaches, and translate research findings into working code.

Strong analytical and problem-solving skills, with the ability to build reliable AI solutions beyond experimental prototypes.

Excellent communication, documentation, and collaboration skills, particularly within distributed teams.

Ability to work independently, manage technical priorities, and adapt to evolving requirements.

Experience with LLM fine-tuning techniques, including LoRA and reinforcement learning-based methods, is preferred.

Knowledge of SQL databases is an advantage.

Familiarity with agent frameworks such as LangGraph or LangChain is beneficial.

Experience with Kubernetes, CI/CD pipelines, and model-serving infrastructure is preferred.

Open-source contributions or experience reproducing machine learning research results is a plus.

Comfortable working a mandatory overnight schedule aligned with US business hours: Monday through Friday, either 7:30 PM–4:30 AM IST or 8:30 PM–5:30 AM IST, depending on US daylight saving time.

Flexibility to occasionally support weekend work or schedule adjustments when required by production needs, client requirements, or business priorities.

Ability to work remotely from India while collaborating with international teams across time zones.

Benefits

Annual compensation ranging from ₹15,00,000 to ₹22,00,000 INR (15–22 LPA), depending on experience and technical expertise.

How Jobgether works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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