AI/ML Engineer

Jobgether; India; On-site; INR 3,000,000 - 4,500,000; Full time

Benefits offered by this job

Remote work

Contract engagement

Advanced AI exposure

End-to-end ownership

Technical growth

Collaborative environment

Continuous learning

Job summary

Jobgether is seeking an experienced ML Engineer to design, develop, and productionize AI/ML and GenAI solutions. You will work across data processing, model development, deployment, and monitoring in a fast-paced cloud environment.

Collaborate with architects, data scientists, and engineers to translate business requirements into scalable AI systems, with emphasis on MLOps, observability, and cost efficiency. Remote, India-based role with growth opportunities.

Qualifications

• Hands-on experience shipping production ML/AI solutions.
• Strong Python proficiency with ML/deep learning frameworks.
• Proven work on Generative AI, LLMs, or RAG projects.
• Experience deploying end-to-end ML pipelines and MLOps.
• Knowledge of embeddings, vector databases, and semantic search.
• Cloud ML platform experience (AWS/Azure/GCP).

Responsibilities

• Design, develop, train, fine-tune, and evaluate ML/LLM solutions for business use cases.
• Build end-to-end AI/ML pipelines from data ingestion to monitoring.
• Develop Generative AI apps including RAG systems.
• Apply prompt and context engineering to improve model performance.
• Design architectures using embeddings and retrieval mechanisms.
• Collaborate with architects, data scientists, and engineers to meet requirements.
• Optimize models for accuracy, latency, and cost across production.
• Implement observability, guardrails, and monitoring for production systems.
• Follow MLOps practices including CI/CD and lifecycle management.

Skills

Python

PyTorch

TensorFlow

scikit-learn

LLMs

MLOps

Cloud ML

Problem solving

Tools

Docker

Kubernetes

VectorDBs

APIs

Job description

This is a hands-on opportunity to build and productionize machine learning and Generative AI solutions for real-world business applications.

You will work across the full AI lifecycle, from data processing and model development through deployment, monitoring, and optimization.

The role combines traditional machine learning and deep learning with modern LLM, RAG, and AI engineering techniques.

You will help transform prototypes into scalable, reliable production systems with measurable business impact.

Working closely with architects, data scientists, and engineering teams, you will translate complex requirements into practical AI solutions.

The environment is fast-paced and collaborative, with strong emphasis on engineering quality, MLOps, scalability, and continuous innovation.

Accountabilities

• Design, develop, train, fine-tune, and evaluate machine learning and LLM-based solutions for business use cases.


• Build and maintain end-to-end AI/ML pipelines covering data ingestion, processing, model development, inference, deployment, and monitoring.


• Develop Generative AI applications, including LLM-powered solutions and Retrieval-Augmented Generation (RAG) systems.


• Apply prompt engineering, context engineering, and LLM fine-tuning techniques to improve model performance, reliability, and relevance.


• Design architectures using embeddings, vector databases, semantic search, and retrieval mechanisms.


• Collaborate with Solution Architects, Data Scientists, and engineering teams to translate business requirements into scalable AI/ML solutions.


• Optimize models and AI applications for accuracy, latency, scalability, reliability, and cost efficiency.


• Implement model evaluation, guardrails, observability, and monitoring for production AI/ML systems.


• Apply MLOps practices including model versioning, CI/CD, deployment automation, and lifecycle management.


• Stay current with developments in Generative AI, LLMs, AI engineering, and machine learning, identifying opportunities to improve existing solutions.


• Contribute to taking AI/ML solutions from prototype through production while maintaining strong engineering and operational standards.

Requirements

• 3-6 years of hands-on experience in Machine Learning Engineering or a closely related role, with a strong focus on practical production delivery.


• Strong proficiency in Python and experience with machine learning and deep learning frameworks such as PyTorch, TensorFlow, and scikit-learn.


• Proven hands-on experience developing Generative AI and LLM-based applications, including LLM fine-tuning, prompt engineering, and context engineering.


• Strong understanding of RAG architectures, embeddings, vector databases, and semantic search.


• Experience building and deploying production-grade ML/AI pipelines across the full development lifecycle.


• Working knowledge of MLOps practices, including model versioning, CI/CD, deployment automation, and model lifecycle management.


• Experience with at least one major cloud ML platform, such as AWS SageMaker, Azure Machine Learning, or Google Cloud Vertex AI.


• Strong understanding of model evaluation, performance optimization, monitoring, and production reliability.


• Ability to assess AI/ML approaches based on accuracy, performance, scalability, latency, and cost.


• Strong engineering mindset with the ability to turn prototypes into maintainable, production-ready solutions.


• Excellent problem-solving, communication, and collaboration skills, with the ability to work effectively across technical and cross-functional teams.


• Ability to operate effectively in a fast-paced, collaborative environment and translate business requirements into practical technical solutions.


• Experience in regulated or highly sensitive domains such as healthcare or government technology is a plus.


• Additional desirable experience includes multi-agent AI systems, orchestration frameworks, LLM evaluation, AI observability, Docker, Kubernetes, APIs, microservices, and scalable cloud-native architectures.

Benefits

• Remote work: Fully remote/offshore position based in India.


• Contract engagement: Opportunity to work on AI/ML initiatives for real-world business applications.


• Advanced AI exposure: Hands-on work with Generative AI, LLMs, RAG, fine-tuning, vector databases, and modern AI engineering practices.


• End-to-end ownership: Opportunity to contribute across the complete journey from AI/ML prototyping to production deployment and monitoring.


• Technical growth: Exposure to modern cloud ML platforms, MLOps, AI observability, and emerging AI technologies.


• Collaborative environment: Work alongside Solution Architects, Data Scientists, and engineering professionals on complex technical challenges.


• Continuous learning: Opportunity to develop expertise in rapidly evolving machine learning and Generative AI technologies.