Senior Machine Learning Engineer (Bangalore/Noida Location)

LevelAI • Bangalore • 11h ago

Why This Job is Featured on The SaaS Jobs

This Senior Machine Learning Engineer role sits at the intersection of SaaS and applied AI, focused on turning high-volume customer interaction data into product capabilities. In the current SaaS landscape, where differentiated workflows increasingly come from LLM-enabled features, work on agents, retrieval, and evaluation is becoming core platform engineering rather than a standalone research function. The company’s Series C stage signals a product that is beyond initial validation and is investing in production-grade AI systems.

From a SaaS career perspective, the role offers exposure to the full lifecycle of shipping ML in a subscription product: model and prompt experimentation, system design for latency and cost, and the operational discipline of monitoring and continual improvement. Experience building RAG pipelines, tool integrations, and scalable inference services transfers well across modern SaaS categories adopting AI, especially where reliability and measurable outcomes matter.

This position is best suited to engineers who prefer applied, end-to-end ownership over isolated modeling work, and who enjoy collaborating across product and customer-facing functions to translate needs into deployable features. It fits professionals who want to deepen practical expertise in agentic systems while maintaining strong grounding in production engineering.

The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.

Job Description

Level AI was founded in 2019 and is a Series C startup headquartered in Mountain View, California. Level AI revolutionizes customer engagement by transforming contact centres into strategic assets. Our AI-native platform leverages advanced technologies such as Large Language Models to extract deep insights from customer interactions. By providing actionable intelligence, Level AI empowers organisations to enhance customer experience and drive growth. Consistently updated with the latest AI innovations, Level AI stands as the most adaptive and forward-thinking solution in the industry.

Empowering contact center stakeholders with real-time insights, our tech facilitates data-driven decision-making for contact centers, enhancing service levels and agent performance. As a vital team member, your work will be cutting-edge technologies, and you will play a high-impact role in shaping the future of AI-driven enterprise applications. You will directly work with people who've worked at Amazon, Facebook, Google, and other technology companies in the world. With Level AI, you will get to have fun, learn new things, and grow along with us. Ready to redefine possibilities? Join us!

Your role at Level AI includes but is not limited to

Build and deploy LLM-powered agents for internal tools and customer-facing products.
Design and improve RAG pipelines, prompt engineering workflows, and tool integrations.
Experiment with open-source/closed-source LLMs.
Implement and evaluate reasoning, planning, memory, and retrieval modules for AI agents.
Optimise ML and LLM systems for accuracy, latency, scalability, and cost.
Build production-ready AI/NLP pipelines with monitoring, evaluation benchmarks, and continual improvement loops.
Collaborate with product, engineering, and customer-facing teams to ship impactful AI features.

We'll love to explore more about you if you have

B.Tech/M.Tech/PhD in computer science or mathematics-related fields from tier-1 engineering institutes with 3+ years of industry experience in Machine Learning and NLP, or applied AI.
Strong programming skills in Python.
Interest and experience with RAG and agentic AI concepts.
Experience with PyTorch, Hugging Face Transformers, and other similar ML libraries.
Interest and experience in model training, inference optimisation, and GPUs.
Experience deploying ML services using REST APIs, Docker, Kubernetes, or cloud platforms.
Exposure to vector databases such as FAISS, Pinecone, Qdrant, or similar.