Principal ML Engineer

Principal ML Engineer
About Nomiso
Nomiso is a product and services engineering company. We are a team of Software Engineers, Architects, Managers, and Cloud Experts with expertise in Technology and Delivery Management. Our mission is to Empower and Enhance the lives of our customers, through efficient solutions for their complex business problems. At Nomiso we encourage entrepreneurial spirit - to learn, grow and improve. A great workplace, thrives on ideas and opportunities. That is a part of our DNA. We’re in pursuit of colleagues who share similar passions, are nimble and thrive when challenged. We offer a positive, stimulating and fun environment – with opportunities to grow, a fast-paced approach to innovation, and a place where your views are valued and encouraged.We invite you to push your boundaries and join us in fulfilling your career aspirations!
Role Overview
We are looking for an ML Engineer with strong hands-on experience in Python-based ML systems and Generative AI solutions. This role focuses on designing and deploying production-grade AI systems, with a strong emphasis on LLMs, RAG pipelines, and MLOps practices.
The ideal candidate will have 14–15 years of overall experience, including at least 10 years of relevant experience in ML and Generative AI.
You will work closely with engineering and product teams to build scalable, secure, and high-performance AI-powered applications.
Key Responsibilities
AI/ML System Design
Design and implement end-to-end ML pipelines (data ingestion → training → evaluation → deployment).
Architect LLM-based solutions using advanced prompting strategies, RAG (Retrieval-Augmented Generation) and agentic workflows.
Define scalable patterns for ML/ GenAI application development.
Model Development & Optimization
Work on data analysis, quality benchmarking, lineage detection and curation, ingestion into vector stores
Work on statistical model training, evaluation, hyper-parameter tuning, feature engineering
Work on fine-tuning of LLMs for specific tasks and prompt optimization (no expectation to build models from scratch at large scale).
Evaluate and select appropriate models (open weights or closed weights).
Collaborate with data teams for feature engineering and dataset readiness.
MLOps & Deployment
Implement MLOps best practices:
Model versioning
Experiment tracking
Monitoring & retraining pipelines
Prompt versioning
Drift detection
Token costs
Handle model deployment in production environments (APIs, batch, streaming).
Ensure performance, scalability, and reliability of AI systems.
Platform & Integration
Integrate AI solutions with existing microservices and backend systems.
Work with vector databases, caching, and APIs for GenAI use cases.
Ensure security and governance in AI deployments.
Collaboration
Partner with product managers and engineers to translate business problems into AI solutions.
Mentor engineers on AI/ML and GenAI best practices.
Must Have Skills
Core
10–15 years of experience in software engineering .
Strong programming skills in Python (mandatory).
Experience in building production-grade ML systems (not just notebooks).
AI/ML & GenAI
Hands-on experience with:
Data Analysis and curation
Feature engineering
Statistical model training, evaluation & hyper parameter tuning
LLMs / GenAI applications
RAG pipeline design
Prompt engineering & model tuning
Experience with frameworks like Tensorflow, PyTorch, Sci-kit, LangChain, LlamaIndex, or similar.
Understanding of embeddings, vector search, and retrieval systems.
Exposure to custom model fine-tuning (good to have, not mandatory).
MLOps & Deployment
Experience with:
Model deployment (API-based or batch)
CI/CD pipelines for ML
Monitoring and logging
Familiarity with tools like MLflow, Kubeflow, or similar (any one is fine).
Cloud & Scalability
Experience with at least one cloud: AWS / Azure / GCP.
Understanding of scalable system design and APIs.
Data & Systems
Working knowledge of databases (SQL/NoSQL).
Experience with vector databases (Milvus, Pinecone, Weaviate, FAISS, etc.).
Good to Have (Optional)
Experience in AIOps or AI for observability/use-case automation.
Background in data engineering or analytics pipelines.
Exposure to Kubernetes/Docker.
Experience in telecom or high-scale product environments.
Location
Hyderabad, Bengaluru (exception: remote if the candidate is extremely good).Some travel will be needed in this position
Website: https://www.nomiso.io/

Work arrangement
No