Top Artificial Intelligence Jobs in Pune

Leads end-to-end power, performance, and area optimization for AI semiconductor SoCs. Influences architecture, RTL, memory hierarchies, dataflows, floorplanning, multi-voltage domains, synthesis, placement, clock-tree synthesis, routing, and power-analysis workflows. Partners with architecture, RTL, physical design, EDA vendors, and software teams to develop custom methodologies and deliver energy-efficient silicon under stringent PPA constraints.

Edit and optimize AI-generated education and test-prep content for accuracy, readability, traditional SEO, AI citation readiness, and product compliance. Responsibilities include fact-checking authoritative sources, updating outdated information, improving metadata, structure, internal linking, and search intent alignment, auditing published articles, enforcing style standards, managing editorial workflows, and providing feedback to improve AI-generated drafts.

Designs and leads Azure AI and TokenOps architectures across Asian markets. Responsibilities include customer workshops, pilots, agent and RAG architecture, model routing and evaluation, observability, FinOps unit economics, enterprise integration, security, Responsible AI, and regulatory compliance. The role coordinates customers, partners, Microsoft stakeholders, and regional delivery teams while creating reusable, locally adaptable architecture and delivery patterns.

Design, build, deploy, and operate production AI/ML systems on Google Cloud. Develop clean, tested Python; evaluate model quality; manage cloud networking and security; and ensure data and models remain within client boundaries. Explain AI trade-offs to clients, collaborate with Google Cloud teams, and own outcomes from prototype through reliable production use.

Build and deploy AI-powered customer solutions across discovery, prototyping, and production. Responsibilities include designing secure multi-tenant and hybrid architectures, integrating identity and access management, encryption, networking, data engineering, CI/CD, observability, and production support. The role partners directly with enterprise customers, executive sponsors, and cross-functional teams to solve ambiguous problems, demonstrate prototypes, and standardize successful solutions. Regular domestic and occasional international travel is required.

Leads a 3–6-person AI-focused engineering team while contributing at least 50% directly to software development. Responsibilities include architecture, full-stack coding, code reviews, testing, documentation, mentoring, delivery, and collaboration with product, design, and QA teams. The role requires commercial LLM experience, strong C# and JavaScript development skills, knowledge of databases, REST APIs, microservices, cloud platforms, and software engineering management.

Lead the architecture, development, and deployment of enterprise AI systems using LLMs, RAG, multimodal models, and multi-agent architectures. Drive prototyping through production delivery, establish LLMOps practices, and ensure model reliability, security, and governance. Serve as a technical advisor during presales, collaborate with product and data teams, and mentor junior engineers.

Join Welo Data’s Swahili contributor network for potential remote AI projects involving text, image, or audio annotation; search, advertising, or chatbot evaluation; prompt creation; and cultural and linguistic accuracy review. Applicants must be native or near-native Swahili speakers based in Kenya, Tanzania, or the Democratic Republic of Congo, with English proficiency, digital-tool comfort, and strong attention to detail. Projects vary in duration and compensation, with details provided before participation.

Develop and deploy AI and machine learning solutions for DNV’s maritime business processes. The role covers stakeholder engagement, data analysis, technical architecture, experimentation, programming, testing, DevOps release cycles, and production integration. The successful candidate will identify new applied AI opportunities, collaborate with data scientists, developers, engineers, and domain experts, and contribute to business value through practical AI systems.

Own the data marketplace product lifecycle, improving vendor onboarding, submissions, quality feedback, offerings management, and buyer discovery. Partner with engineering, research, GTM, and operations to convert complex requirements into reliable workflows. Define marketplace health and adoption metrics, use qualitative and quantitative insights to prioritize product improvements, and guide launches and iteration in a fast-growing AI startup.

Develop AI-driven products through hands-on software development and product engineering. Build minimum viable products, solve problems creatively, and help shape future AI applications. The role is available remotely or in Boston and Chicago, with contract options ranging from two weeks to three months or full-time, plus potential revenue sharing.

Provides systems safety engineering for aircraft turbine engines and power systems. Conducts hazard, reliability, and root cause analyses; develops safety requirements, plans, procedures, and reports; reviews designs for regulatory compliance; manages requirements using DOORS; supports certification and qualification; collaborates cross-functionally on risk mitigation, reliability metrics, process automation, and AI-enabled safety improvements.

Drive enterprise business development by engaging CXO-level stakeholders, building strategic relationships, generating sales pipelines, positioning AI, data, cloud, and hyper-automation solutions, managing end-to-end sales cycles, negotiating agreements, and closing deals. The role requires strong executive communication, networking, analytical thinking, resilience, and technology-sector sales experience.

The Engineering Specialist is responsible for frontend application development and backend integration, focusing on building scalable web applications, collaborating with teams, and adhering to architecture standards.

Designs, develops, and deploys autonomous and multi-agent AI systems for business workflows. Builds agentic applications using LLMs, RAG, vector databases, tool calling, memory, and orchestration frameworks. Integrates agents with enterprise applications, APIs, databases, CRMs, and ERPs while developing backend services and APIs. Optimizes solutions for scalability, security, performance, and cost, and collaborates with product, engineering, and business teams to identify automation opportunities.

Own the end-to-end product experience for agentic AI products, including roadmap, feature definition, UX optimization, onboarding, and in-app education. Analyze product usage and customer feedback to identify friction, simplify workflows, improve conversion, and prioritize high-impact improvements. Partner with design and engineering while independently driving product direction, creating detailed PRDs, tickets, prototypes, and designs in a fast-moving startup environment.

Develop and deploy multilingual automatic speech recognition systems for children’s reading assessment. Responsibilities span speech-data collection and annotation, audio preprocessing, model training and fine-tuning, decoding, forced alignment, error analysis, benchmarking, and deployment in low-bandwidth or offline environments. The role requires adapting modern ASR architectures, detecting reading errors, evaluating WER/CER and alignment quality, and collaborating with assessment and field teams to translate model outputs into reading fluency and accuracy measures.

Lead the design, development, deployment, and optimization of production-grade Generative AI systems. Build RAG pipelines, fine-tune foundation models, develop AI agents, establish evaluation and observability frameworks, and integrate multimodal AI capabilities. Collaborate with product, engineering, data, and business stakeholders to deliver scalable, reliable, secure, and responsible AI solutions.

Provides technical leadership for FAA and EASA certification and qualification of aircraft turbine engines and auxiliary power units. Prepares and reviews certification and qualification plans, reports, and related artifacts; coordinates with aviation regulators; standardizes and improves certification processes; and applies automation and artificial intelligence to reduce cycle time and cost. Supports systems engineering activities, talent development, technical coaching, and innovation across Honeywell Aerospace programs.

Designs, develops, and implements scalable AI solutions and infrastructure platforms. Responsibilities include maintaining AI models and algorithms, automating data transformation and validation, generating and maintaining test scripts, refactoring legacy code for cloud-native environments, monitoring system reliability, managing incidents, and applying security and software engineering best practices. The role collaborates with architects, data engineers, data scientists, and product managers while contributing to technical direction, risk management, and team development.

Build and deploy AI and machine learning solutions using distributed computing, cloud platforms, data pipelines, and production-grade software engineering practices. Responsibilities include technical requirements analysis, feature development, testing, code reviews, agile collaboration, architecture validation, and prototyping emerging technologies. The role partners with data scientists, architects, client-facing teams, and clients to deliver scalable AI solutions and manage machine learning lifecycles.

Build production AI/ML systems for extracting structured data from complex documents. Responsibilities include document ingestion, OCR, confidence modeling, RAG knowledge bases, agentic workflows, human-in-the-loop review, reprocessing and diffing, model optimization, evaluation, monitoring, and governance. The role also provides technical mentorship to mid-level AI engineers and owns systems end to end.

Designs and deploys production-grade AI agents, RAG systems, multi-agent workflows, SDKs, and scalable LLM applications. Builds secure knowledge architectures, integrations, MLOps and LLMOps pipelines, governance controls, and evaluation frameworks. Serves as a technical authority in client engagements, mentors engineers, supports presales and recruitment, and drives engineering standards across AI projects.

Designs and develops scalable automated testing solutions for carrier-grade, cloud-native network products. Responsibilities include building functional, regression, performance, and end-to-end test frameworks; developing tests with Python, Java, or Go; integrating testing into CI/CD pipelines; applying AI/ML for test generation and defect analysis; analyzing network and test data; and collaborating with development, product, and QA teams.

Designs and develops scalable automated testing solutions for carrier-grade, cloud-native network products. Responsibilities include building functional, regression, performance, and end-to-end test suites; integrating testing into CI/CD pipelines; applying AI/ML for test generation, anomaly detection, defect prediction, and self-healing automation; analyzing network and test data; and collaborating with development, product, and QA teams.