Top Artificial Intelligence Jobs in Mumbai

Lead design, deployment, and management of prompt-based LLM models for NLP in financial services. Build data pipelines, MLOps tooling, and APIs; research prompt engineering and orchestration techniques; evaluate and optimize model performance; collaborate with cross-functional teams and communicate results to stakeholders.

Develop, configure, and deploy conversational AI solutions using Kore.ai XO10, XO11, or Artemis. Build chatbots and AI agents, integrate LLMs, RAG, vector databases, enterprise applications, and data sources, and implement agentic AI and multi-agent orchestration. Develop REST API integrations with JavaScript, Node.js, Python, and JSON. Troubleshoot production issues, improve performance and accuracy, apply governance standards, and collaborate with business, technology, and product teams.

Develop and maintain Python applications, APIs, workers, integrations, and AWS Lambda functions. Manage AWS and Linux environments, infrastructure automation, deployments, CI/CD pipelines, and production support. Improve reliability through monitoring, canary releases, rollback, and recovery practices. Maintain databases, messaging, authentication, security, dependencies, and external integrations. Apply AI-assisted development tools with human review and security validation. The role requires ownership of software delivery from application code through production.

Transcribe unscripted Odia conversational audio verbatim in Odia script for AI speech model training. Responsibilities include preserving fillers and speech patterns, handling Odia-English/Hindi code-switching, tagging audio issues, following formatting guidelines, maintaining 97%+ accuracy, meeting turnaround times, and revising work based on QA feedback.

Supports development, fine-tuning, deployment, and monitoring of generative AI and machine learning models for cybersecurity applications. Responsibilities include data extraction and analysis, threat intelligence support, backend and API integration, preprocessing, visualization, testing, debugging, documentation, and performance optimization using multiple programming languages and frameworks.

Develop and deploy AI/ML applications, LLM integrations, automation workflows, chatbots, recommendation systems, and intelligent assistants. Build RAG solutions using prompt engineering and vector databases, integrate AI with backend systems and APIs, optimize models, and support testing, monitoring, documentation, and technical design. Collaborate with product, engineering, cybersecurity, and business teams in a fast-paced product environment.

Leads consultative presales for credit and fraud risk solutions across Asia Pacific. Partners with clients and sales teams to discover challenges, develop proposals, business cases, platform demonstrations, executive presentations, and deal strategies. Serves as a subject matter expert across the risk lifecycle, payment ecosystems, authorization, authentication, lending, fraud, and collections. Supports go-to-market initiatives, innovation workshops, thought leadership, and proof-of-concept engagements, with travel of up to 50%.

Architect and deliver generative AI solutions centered on LLM training, optimization, deployment, and RAG workflows. The role involves customer collaboration, technical workshops, pre-sales presentations, solution integration, and technical leadership. Responsibilities include fine-tuning models, optimizing GPU-based inference, designing scalable training workflows, and partnering with NVIDIA engineering teams. Candidates need deep expertise in language models, TensorFlow, PyTorch, Hugging Face, GPU clusters, and production deployments, with cloud, containerization, and Kubernetes experience preferred.

The ServiceNow Solution Architect - AI role involves leading and mentoring a team, designing and deploying AI-driven applications, managing application development, and integrating solutions into ServiceNow modules. Responsibilities include coordinating with stakeholders, troubleshooting, and ensuring adherence to technical standards.

Develop, train, deploy, and maintain machine learning and generative AI solutions, including predictive models, NLP applications, chatbots, LLMs, and agentic AI systems. Design scalable ETL pipelines, optimize databases, process large datasets with Spark and Snowflake, and create business visualizations. The role requires expertise across Python, SQL, cloud AI platforms, machine learning frameworks, databases, and data engineering.

Develop NLP and generative AI models for text classification, sentiment analysis, entity recognition, translation, and semantic analysis. Responsibilities include text preprocessing, feature engineering, model training and evaluation, exploratory data analysis, algorithm optimization, and large-scale data processing. Collaborate with engineering and domain teams to integrate solutions, document methodologies, and apply emerging NLP and machine learning techniques.

Designs, deploys, operates, and optimizes NVIDIA and AMD GPU clusters for LLM training, inference, and HPC workloads. Responsibilities include Linux administration, kernel and driver tuning, Kubernetes orchestration, CUDA and GPU networking configuration, Slurm and MPI operations, infrastructure automation, monitoring, incident analysis, and customer support. The role owns deployments from architecture through production acceptance and requires strong cross-layer debugging, documentation, and collaboration skills.

Manage GPU cloud product catalogues, pricing models, SKU documentation, competitor research, vendor evaluations, product collateral, and launch support. Collaborate with engineering, finance, sales, marketing, operations, and technology partners to maintain accurate commercial information, identify portfolio opportunities, coordinate product launches, and monitor post-launch adoption and market feedback.

Lead implementation and operations of enterprise observability and AIOps platforms (Elastic stack preferred). Onboard infrastructure, cloud, and applications; build dashboards, alerts, ingestion pipelines; support incident investigation, automation, IaC, upgrades, and runbook documentation to improve reliability and operational readiness.

Build and improve production LLM agents and AI features through evaluation, experimentation, observability, prompting, context engineering, tool use, and workflow design. Develop backend services, APIs, data models, and feedback pipelines that make agent behavior reliable and measurable. Investigate performance issues, run staged rollouts and production replays, define quality standards, and partner with Product, Data Science, and Sales to deliver business outcomes with appropriate privacy, security, and human-oversight safeguards.

Own the AI product roadmap from discovery through launch and iteration. Define model evaluation standards, failure modes, rollback plans, and agent-autonomy boundaries. Prototype and ship production AI features, influence architecture, and apply prompting and context design. Collaborate with Design, Research, Engineering, Sales, Marketing, and Customer Success while tracking quality, latency, cost, adoption, and growth metrics.

Designs, optimizes, and maintains ComfyUI workflows for audiovisual generative AI models across production and R&D. Integrates emerging tools, develops reusable templates, tests model inference environments, and collaborates with research, pipeline, DevOps, technical, and creative teams. The role also supports ComfyUI platform development and trains artists in generative AI workflows.

Create environments, characters, props, moods, and visual storytelling concepts for films, games, and transmedia projects using traditional art and AI tools. Interpret creative briefs, rapidly iterate visual styles and compositions, refine AI-generated imagery into production-ready assets, and collaborate with art, VFX, 3D, and animation teams. Maintain stylistic consistency with project vision and IP guidelines while contributing to worldbuilding, pitch visualizations, and innovative AI-assisted workflows.

Develops and evaluates audiovisual generative AI technologies, prototypes proof-of-concept workflows, tests research developments, and builds scalable ComfyUI pipelines. The role manages training datasets, model tuning, inference environments, and custom nodes while collaborating with research, product, developers, and creatives. It also presents findings and trains artists across disciplines.

Leads AI and data science strategy, model development, technical exploration, deployment, and post-production performance management. Manages and mentors a team of data scientists, prioritizes projects and resources, partners with stakeholders, and promotes AI literacy. The role requires expertise in machine learning, MLOps, Vertex AI, generative and agentic AI, stakeholder engagement, and people leadership.

Build and enhance end-to-end business applications supporting the Palantir engineering portfolio. Responsibilities include designing, developing, testing, troubleshooting, and optimizing scalable .NET applications; collaborating with engineers, architects, product partners, and stakeholders; contributing to code and design reviews; improving DevOps and automation practices; resolving production issues; and mentoring less experienced team members.

Leads enterprise AI and automation activation from use-case discovery through adoption and scaled deployment. Partners with business, product, engineering, data science, and vendor teams to define requirements, deliver AI agents and workflows, manage publishing and testing, establish responsible AI governance, track KPIs and value realization, monitor performance and adoption, and drive continuous improvement across supply chain and other functional domains.

Own product marketing for Cyara’s Agentic AI assurance products, including positioning, messaging, go-to-market launches, competitive strategy, sales enablement, technical content, analyst and press engagement, and pipeline measurement. Partner closely with Product, Sales, Demand Generation, and Customer Success to translate technical capabilities into compelling enterprise-market narratives. The role requires experience in B2B SaaS or AI product marketing, enterprise sales-led motions, technical audiences, and measurable product launches.

Support sales by positioning products, demoing solutions, mapping client requirements to Signzy offerings, creating proposals and pitch decks, performing business analysis and scoping, coordinating with product/project teams, managing client expectations, and handing off to delivery post-signoff. Train sales on product, compliance and technology for assigned use-cases.

Develop, fine-tune, deploy, and optimize AI/ML models and LLM-based RAG solutions using Python and AWS Bedrock. Build REST APIs and microservices with FastAPI or Flask, deploy scalable cloud infrastructure, manage containerized workloads, and develop data pipelines and ETL processes. Work with relational and NoSQL databases, CI/CD, and AWS services while collaborating with technical and product teams and documenting AI workflows.