AI Jobs in India

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As a Forward Deployed Engineer - Agents, you will lead the end-to-end implementation of AI Virtual Agents and CX automation workflows for customers, owning the entire process from discovery and scoping through launch and optimization. Responsibilities include configuring agent workflows, decision logic, and automation behaviors to maximize accuracy, reliability, and business outcomes; implementing guardrails and validation frameworks to ensure safe, compliant, and predictable agent performance; building, testing, and validating integrations with enterprise systems such as CRM, ticketing, telephony, and data platforms; partnering with customer technical stakeholders to define success criteria, gather requirements, and deliver against timelines; translating customer needs into clear implementation plans and documentation; running tight feedback loops with Engineering and Product to improve platform capabilities; and collaborating with Product, Engineering, Design, and GTM teams to deliver repeatable, best-in-class deployments.

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Design, develop, test, deploy, maintain, and improve scalable, secure, and high-performance backend systems with a focus on high availability, low latency, and cost-effectiveness. Act as the subject matter expert in infrastructure when designing new products and introducing new technology to existing products. Collaborate closely with engineering and research teams to integrate infrastructure components with product features to optimize system performance and user experience. Design event-driven architectures and develop APIs and microservices for real-time processing and analytics. Ensure system reliability, performance, and scalability through monitoring, logging, and error handling. Stay current with emerging trends, technologies, and methodologies to enhance infrastructure capabilities. Participate in code reviews, contribute to open-source projects, and mentor junior engineers.

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The Principal Applied AI Researcher is responsible for setting the company-level technical direction for domain-specific model strategy, defining how Articul8 builds, evaluates, scales, and sustains model superiority across continued pre-training, fine-tuning, post-training, and release quality standards. They architect the agentic model development paradigm by designing the agent-orchestrated research infrastructure to enhance research capabilities. The role involves leading deep research on model adaptation methodology, data curation strategies, post-training methods, and training dynamics while deploying agentic systems for exhaustive studies and failure analyses. Additionally, they shape model strategy across all domains and verticals of the company, prioritizing new model domains through agent-driven competitive intelligence and market analysis. They define evaluation strategy, including benchmark design, expert assessments, model failure analysis, and robustness standards, building always-on evaluation systems. The researcher leads cross-cutting research initiatives to strengthen the model layer, influences platform-level decisions about model lifecycle management, portfolio strategy, release criteria, and integration architecture. They mentor senior researchers, coach on agent-augmented research design, and raise technical judgment and rigor. Lastly, they maintain hands-on research impact through publications, patents, and visible output, exemplifying the use of massively parallel agentic systems for groundbreaking research.

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Set technical direction for the data platform by owning the architecture roadmap for Fiddler's ingestion, storage, and query layers. Drive multi-quarter initiatives from problem framing through design, implementation, and rollout. Design systems for 10x scale by leading the evolution of the ClickHouse-backed analytics layer and Kafka-based ingestion pipeline to handle significant growth in event volume, query complexity, and tenant count. Define the event model for next-generation AI workloads by architecting the data model and storage strategy for agentic application traces, LLM evaluation pipelines, and enrichment workflows, balancing flexibility, query performance, and schema evolution. Drive cross-team technical decisions by partnering with Backend, Monitoring, and Enrichment teams to ensure platform abstractions meet their needs and represent the Platform perspective in company-wide architecture reviews. Own platform reliability and cost efficiency by establishing SLOs, capacity planning processes, and cost optimization strategies for data infrastructure, and making build-vs-buy decisions for infrastructure components. Raise the engineering bar by mentoring senior engineers and establishing patterns and guardrails including data modeling conventions, query optimization practices, and testing strategies that have team-wide impact. Lead by example in code review, design documentation, and incident response. Influence product direction by working with Product and Customer Engineering to translate customer data challenges into platform capabilities and help define priorities and risks for future work.

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The role involves contributing to the development of next-generation, high-performance machine learning serving systems. Responsibilities include building infrastructure that powers AI applications, working on problems at the intersection of distributed systems, machine learning, and high-performance computing, and solving fundamental computer science problems impacting AI deployment. Specific projects include implementing asynchronous inference for non-blocking client requests, designing intelligent request routing systems to balance load across thousands of model replicas with strict latency SLAs, building traffic management systems for zero-downtime model updates handling terabytes of inference requests, improving state management for scale from thousands to tens of thousands of replicas, architecting frameworks for multi-model orchestration in complex ML pipelines ensuring end-to-end latency guarantees, and developing observability and debugging tools for distributed ML applications at scale. The work involves writing performance-critical code in Python (with Cython optimizations) and potentially C++, working with distributed systems at scale using Ray Core's actor system, gRPC, and custom networking protocols, extending cloud-native infrastructure such as Kubernetes and service meshes, gaining system-level knowledge of ML/AI frameworks like TensorFlow, PyTorch, JAX, and transformers, and ensuring production reliability with tools like OpenTelemetry, Prometheus, distributed tracing, and chaos engineering to maintain 99.99% uptime. The role also involves leveraging AI coding agents to enhance team productivity while maintaining high code quality standards.

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Ship critical infrastructure by managing real-world logistics and financial data for the largest enterprise in the world. Own the why by building deep context through customer calls and understanding Loop’s value to customers, pushing back on requirements if a better, faster solution exists. Work across system boundaries with full-stack proficiency, including frontend UX, LLM agents, database schema, and event infrastructures. Leverage AI tools to automate boilerplate work, focusing on quality, architecture, and product taste. Constantly optimize development loops, refactor legacy patterns, automate workflows, and fix broken processes to raise the velocity bar.

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Build agents as modular, plug-and-play components that slot cleanly into the wider stack. Add memory layers (short-term, long-term, summarization, retrieval-backed) into running systems. Wire up tool integrations, MCP servers, and skills. Own the quality of the features you put out, including tests, evals, and observability. Analyze production traces to understand system behavior and implement fixes accordingly.

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As a Product Backend Engineer, design and operate backend systems that enable AI capabilities to deliver seamless and dependable product experiences. Build secure, multi-tenant services, orchestrate interactions with large language models (LLMs) and agentic tools, and define backend architecture for expanding product offerings. Collaborate with Product to prioritize customer-focused work and deliver reliable features quickly. Design and own backend services and APIs supporting web applications, workflows, and integrations. Model and manage data in Postgres and related data stores to ensure low-latency and reliable user experiences. Build permissions-aware systems with appropriate auditing for enterprise and government customers. Implement backend features that interact with AI systems via robust, well-structured services. Collaborate with frontend, product, and design partners for solution design, API contract definition, and end-to-end feature delivery. Add logging, metrics, and tracing for service observability and on-call readiness. Improve performance and scalability by profiling, tuning, and refining service boundaries. Participate in code reviews, technical design discussions, and an on-call rotation for owned services.

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The AI Deployment Engineer serves as the primary technical subject matter expert on OpenAI Codex for a portfolio of customers, embedding deeply with them to enable their engineering teams and build coding workflows. They partner directly with customers to design and implement AI-enhanced development workflows, from rapid prototyping through scalable production rollout. The role involves building high-quality demos, reference implementations, and workflow automations using Codex as part of the development process. The engineer leads large-format workshops, technical deep dives, and hands-on enablement sessions to help engineering organizations adopt AI coding tools effectively and safely. They contribute technical content including examples, guides, patterns, and best practices to the OpenAI Cookbook to help the broader developer community accelerate their work with Codex. Gathering high-fidelity product insights from real customer deployments, they translate these insights into clear product proposals and model feedback for internal teams. The engineer influences customer strategy and decision-making by framing how AI coding tools fit into their software development life cycle, technical roadmap, and organizational workflows. They also serve as a trusted advisor on solution architecture, operational readiness, model configuration, security considerations, and best-practice adoption.

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Partner with Account Executives to discover and scope customer challenges, designing high-value technical solutions that showcase the ROI of Netomi’s platform. Architect and build agentic workflows that integrate generative AI with APIs, databases, and enterprise tools to power experiences for customers' end users. Develop custom demonstrations, prototypes, and proofs of concept using the Netomi platform tailored to specific clients' use cases. Design, test, and refine prompts and AI orchestration chains to optimize performance, reasoning, and reliability across varied use cases. Communicate complex technical concepts clearly and persuasively to audiences ranging from C-level executives to hands-on engineers. Collaborate with product and engineering teams, contributing insights from customer engagements to inform roadmap priorities. Document and present solution designs, workflows, and technical configurations for both internal and client-facing reference.