Senior Machine Learning Engineer

Snow Planet

5 - 10 years

7 - 12 Lacs

hyderabad

Posted:12 hours ago| Platform:

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Skills Required

prototype agile development machine learning information retrieval application development data quality open source load balancing monitoring python

Work Mode

Work from Office

Job Type

Full Time

Job Description

What you get to do in this role:

Own the end to end lifecycle of models (problem framing data training evaluation deployment monitoring).

Design features and labeling strategies; improve data quality with heuristic and programmatic techniques (e.g., weak supervision, active learning, synthetic data generation via LLMs).
Train, tune, and compare models (tree based, linear/GLM, deep learning, seq2seq, recommendation,LLMs) with reproducible pipelines.
Build and tune approriate information retrieval for RAG use cases (e.g., dense and sparse retrieval), engineering the right context for the task.
Implement rigorous offline metrics and online A/B experiments; define guardrails and SLOs for quality, latency, and cost.
Develop efficient API endpoints for model inference and ensure they are scalable and production ready (e.g., containerization, load balancing, auto-scaling, monitoring, logging, alerting, and incident response) and meet product requirements.

To be successful in this role you have:

Experience with AI developer productivity tools like Windsurf, Cursor, and prompt tuning is required, along with the ability to apply AI techniques to practical engineering problems.
We are seeking individuals who can quickly prototype, optimize prompts, and develop scalable, impactful AI-driven solutions.
5+ years of hands on experience in ML/AI engineering, with a strong focus on building and deploying AI/GenAI applications.
Proficiency in Python (ML libraries, GenAI/LLM frameworks) and Java (enterprise application development, OOP).
Practical experience developing with LLM frameworks such as LangChain, LangGraph, and vendor SDKs/APIs (OpenAI, Anthropic, etc.).
Experience in using Al productivity tools such as Cursor, Windsurf, etc. is a plus or nice to have
Experience with methods of training and fine-tuning large language models, such as distillation, supervised fine-tuning, and policy optimization is a plus or nice to have.
Strong skills in prompt engineering and designing agentic reasoning pipelines.
Solid understanding of ML evaluation techniques and experience in implementing model monitoring & metrics.
Proven ability to debug and optimize inference pipelines for performance and cost efficiency.
Strong problem solving skills with the ability to work in fast paced, agile development environments.
Bonus: Contributions to open-source projects, blogs, or technical papers in LLM/GenA

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