Top Artificial Intelligence Jobs in Pune

Investigate escalated production incidents (DB anomalies, failed payments, data migrations), write post-mortems and runbooks, and build AI-powered automation (LLM diagnostics, agents) plus developer tooling. Maintain CI/CD, GCP infrastructure, container deployments, observability, and partner on ERP and payment provider integrations.

Lead Android architecture and infrastructure for Binance mobile apps: design, build, optimize, and maintain high-performance Kotlin/Java applications, create shared libraries and toolchains, collaborate on hybrid JavaScript runtimes, and use profiling and AI-assisted debugging to troubleshoot and improve app performance.

Early-career Data Scientist role in Binance Accelerator Program focused on designing and evaluating AI models to detect P2P scams. Responsibilities include developing CV-based document verification, LLM-based detection and interpretable explanations, image-embedding feature extraction, and collaborating with engineers to optimize detection pipelines and deliver insights.

Join a 3-6 month accelerator to design, implement, and optimize AI agent architectures. Develop agent-tool integrations, human-AI collaboration mechanisms, monitoring/evaluation systems, and collaborate with product teams while staying current on agent research.

Design, develop, and optimize high-performance Java backend systems. Contribute to architecture, implement core modules, troubleshoot production issues, perform code reviews, and improve scalability, availability, and reliability of distributed financial or blockchain systems.

Lead data analysis and BI for recommendation, feeds, and growth use cases; deliver analytics, reporting, and end-to-end solutions; prioritize stakeholder requests; partner cross-functionally to deliver high-impact use cases; implement LLM-based agents for analysis, chatbot interaction, and sentiment monitoring; and mentor junior analysts.

Build AI-driven developer tools: coding assistants, knowledge retrieval, workflow automation, and LLM-powered apps for code generation, understanding, and test automation. Collaborate with engineering teams to identify pain points, ship solutions, and experiment with prompt engineering, RAG, and AI agents.

Design, build, and deploy production AI agent workflows and LLM-powered systems. Integrate tools, APIs, databases, and internal services; improve agent quality, latency, safety, and cost. Collaborate with product and operations to translate business problems into practical AI solutions and contribute to backend services and evaluation/guardrail patterns.

Lead QA for LLM-based consumer products: design and execute end-to-end testing, verify LLM correctness, consistency, stability, and safety, perform regression and risk assessments for model updates, and build automated QA tooling, leveraging AI/LLM to enhance test design and defect analysis.

Early-career full-time program offering training, mentorship, and hands-on work within Binance's blockchain ecosystem. Participants develop practical skills in Web3, collaborate across teams, and contribute to product and engineering projects while receiving tailored curriculum and 1:1 mentorship.

Lead a Data Analyst team to analyze PB-scale transactional and customer data, build algorithmic systems and visualizations, implement ML/LLM solutions for compliance use cases, support business stakeholders with data requests, and translate findings into actionable recommendations.

Design and implement backend systems for a user-facing AI chat product. Collaborate with product, DevOps, and QA; drive best practices, conduct code reviews, and deliver high-quality microservices using cloud infrastructure and message brokers.

Early-career role building AI-powered developer tools and internal LLM applications. Tasks include coding assistants, lightweight RAG pipelines, prompt optimization, testing, documentation, and researching emerging AI technologies to improve engineering productivity.

Early-career software engineer in a fixed-term accelerator contributing to backend/full-stack feature development, testing, CI/CD, and deployments. Collaborate on API and system design, integrate and deploy AI/LLM capabilities (LLM APIs, RAG, embeddings, agents), build datasets/benchmarks, monitor performance, debug issues, and produce technical documentation.

Lead end-to-end execution of strategic AI and technology initiatives, driving Agile delivery across engineering, product, data, and infrastructure teams. Remove blockers, manage dependencies, align stakeholders, and accelerate organization-wide AI adoption while fostering a high-ownership, innovation-driven engineering culture.

Six-month early-career internship building LLM-powered agent workflows and production AI components. You'll design prompts, retrieval and tool-use logic, run benchmarks, improve quality/latency/reliability/safety, contribute to backend services/APIs, and ship working prototypes alongside full-time engineers.

Build internal AI developer productivity tools (coding assistants, retrieval systems, workflow automation). Develop LLM-powered apps for code generation, understanding, and test automation. Integrate AI across the SDLC, embed with teams to remove friction, experiment with prompt engineering, RAG, and AI agents, and evaluate emerging AI technologies.

Design, build, and maintain high-performance backend services and APIs using Java/Spring Boot. Develop and productionize recommendation algorithms and LLM systems, optimize LLM workloads (including on-prem), ensure scalability, reliability, and security, and collaborate across time zones with product and project teams to own end-to-end architecture.

Early-career research role designing and running experiments on LLM reasoning, post-training alignment, and test-time scaling. Implement model variants and training pipelines in PyTorch/Hugging Face, evaluate on crypto-native data, maintain experiment tracking (W&B), and partner with engineering to translate research to production.

Lead planning, design, delivery and promotion of a customer-service chatbot management platform. Use AI and big data to improve CS operations (corpus training, SOPs, assistant replies, summaries). Collaborate with engineering, UX, and CS teams. Monitor KPIs and user cases to drive product improvements.

Lead AI-driven growth product strategy to improve acquisition, activation, retention, cross-sell, and churn reduction. Design viral loops and referral programs, use data and user insights to inform features, manage roadmaps, run experiments, and collaborate with marketing, operations, and engineering to optimize user funnels and drive scalable organic growth.

Early-career role contributing to design and development of AI agents, building benchmark datasets, prototyping agent workflows (e.g., LangChain/LangGraph), and collaborating with senior engineers on system integration and deployment.

Design and build internal AI productivity tools and LLM-powered applications to augment developer workflows. Integrate AI into the software development lifecycle, apply techniques like prompt engineering, RAG, and agents, partner with engineering teams to identify bottlenecks, and continuously evaluate emerging AI technologies to improve internal platforms and engineering efficiency.

Support development of internal LLM-based tools (coding assistants, RAG pipelines, prompt optimization), improve engineering efficiency, test and document AI tools, and research emerging AI technologies.

Design and develop high-performance backend services for an AI-powered trading assistant: build AI agent execution, trading order execution, AI sub-account and API key lifecycle management, integrate Binance trading APIs, implement skills/tool-calling frameworks, and ensure scalable, low-latency real-time data pipelines, observability, and security.