Software Engineer 2 , Machine Learning

Posted an hour ago

India

2-5 years experience

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What will excite us?

MIMP: An AI-first approach for solving problems.
Hands-on experience building statistical and machine learning models, with proven expertise across traditional methods (regression, classification, clustering, tree-based models) and deep learning techniques (neural networks, CNNs, RNNs).
Solid prompt engineering skills.
Hands-on experience building and deploying AI Agents using LangChain, LangGraph, OpenAI API, Gemini API, Anthropic API, etc.
Hands-on experience with multi-step agent workflows, orchestration, and evaluating agent reliability.
Hands-on experience with vector databases (Pinecone, Weaviate) and semantic search.
Hands-on experience fine-tuning open-weight language models and successfully deploying and maintaining them in scalable production environments on cloud platforms (e.g., AWS, Azure, GCP).
Good debugging and online searching skills.
Good communication skills.
Good problem-solving skills (ability to solve hard-level data structures and algorithms problems).

What will you be doing?

Collaborate closely with product managers and business stakeholders to gain a deep understanding of business challenges, translating them into well-defined machine learning problems and actionable project requirements that drive business growth and enhance learner experiences.
Leverage your expertise in statistical modeling, machine learning, and generative AI/LLMs to research and design optimal solutions for the identified machine learning problems.
Collaborate closely with machine learning engineers to develop and refine optimal solutions for the identified machine learning problems.
Closely work with Applied AI/ML engineers building LLM-powered agentic systems for real product use cases.
Design, develop, and optimize agent pipelines/APIs, build tests for agent behaviors, and contribute to evaluation frameworks.
Take ownership of deploying the developed machine learning solutions into scalable production environments (on cloud platforms like AWS, Azure, GCP) and ensure these deployed solutions are effective.
Staying informed about recent developments in AI/ML, including key publications, best practices, evaluation methodologies, technology stacks, and relevant tools.