Top Machine Learning Jobs in Hyderabad

Develop and refine voice-optimized prompts and agentic LLM workflows for finance use cases. Build RAG grounding, integrate LLMs with voice/telephony (LiveKit), implement feedback loops, A/B testing, fine-tune models, and collaborate on production ML services and OCR/chatbot components.

Develop quantitative models for cryptocurrency derivatives and spot markets, conduct statistical and econometric analysis, build pricing and risk models, and backtest systematic trading strategies. Collaborate with trading and product teams to validate and deploy models, monitor performance, analyze high-frequency data, and communicate research insights to leadership and trading desks.

Train and deploy small, task-specific ML models for realtime English conversational-skill applications. Create datasets and task-specific evaluations, improve internal SOTA, develop ASR and speech-feedback metrics, iterate experiments, ship production systems, and collaborate cross-functionally while helping hire as the team grows.

Conduct independent research on frontier AI evaluation and auditing. Design rigorous evaluations, audit methods, and reproducible experimental pipelines; analyze model capabilities, risks, robustness, and alignment-relevant behavior. Produce audit reports, recommendations, publications, tools, and presentations while collaborating with technical, policy, engineering, and standards teams. The role requires careful handling of confidential information and may involve pilot audits with frontier AI companies.

Develop and enhance ServiceNow CMDB, ITOM, and ITSM solutions with AI-driven automation, AIOps, GenAI, intelligent incident routing, and predictive insights. Build workflows, integrations, applications, and automation using JavaScript, Glide scripting, REST APIs, Flow Designer, Business Rules, and Script Includes. Configure Discovery and Event Management, translate business requirements into scalable solutions, collaborate with stakeholders, and optimize existing ServiceNow applications while following security, governance, performance, and scalability standards.

Build privacy and anonymization systems that detect and transform PII, credentials, secrets, and other sensitive information in data used for AI training. Develop detection methods combining rules, statistical models, classifiers, and LLMs; create production data-processing pipelines; evaluate privacy risk and retained utility; and ensure robustness against schema drift, unusual formats, leakage, and adversarial re-identification.

Research Engineer applying frontier machine learning and reinforcement learning research to production AI capabilities. Responsibilities include model design, training, evaluation, RLHF, multi-objective optimization, LLM post-training, reward modeling, auditing, and translating prototypes into reliable systems. The role also involves customer engagement, technical communication, and shaping research and product strategy.

Lead technical support for B2B customer deployments, diagnosing issues across ML models, applications, APIs, infrastructure, networking, and deployment environments. Investigate incidents, review logs and configurations, coordinate escalations with FDE, SRE, and Engineering teams, monitor deployment health, document root causes, communicate with customer stakeholders, and develop tools and dashboards to meet support SLOs.

Hiring for multiple software engineering opportunities in Hyderabad, including fresher and experienced positions across Java, testing, Python, DevOps, AWS, full-stack development, data science, AI/ML, cybersecurity, Node.js, and React.js.

Lead the development of enterprise AI solutions using Generative AI, LLMs, RAG, and Agentic AI. Build AI applications, chatbots, and retrieval systems using embeddings and vector databases. Apply machine learning frameworks, deploy and monitor AI models, establish CI/CD pipelines, and design scalable enterprise AI architectures across cloud platforms.

Build predictive and customer-satisfaction models, translate business questions into analytics projects, and develop production-grade machine learning solutions. Collaborate with data scientists and stakeholders, communicate analytical insights, and work with R, Python, Hadoop, Redshift, Spark, Azure, Azure ML, Terraform, and CI/CD pipelines. Preferred work includes PyTorch and image-model development and deployment.

Develops full-stack AI and machine learning applications using web technologies, statistical modeling, deep learning, databases, cloud platforms, and APIs. Designs algorithms, translates technical specifications into code, performs testing and debugging, documents solutions, and improves product and system performance. Works independently on complex initiatives, collaborates with stakeholders, mentors colleagues, and may manage projects or review others’ work.

Design, build, and maintain backend APIs and services for a real-time supply chain visibility platform. Own system architecture, real-time event ingestion, data normalization, ML integration, monitoring, and production debugging while collaborating with Product and Data Science to deliver scalable, high-availability features.

Design, prototype, and deliver agentic AI solutions using ML and generative models. Partner with product, engineering, and delivery teams to build, evaluate, and deploy LLM-based solutions, run experiments and POCs, and communicate technical recommendations to stakeholders.

Own end-to-end enterprise sales across Southeast Asia: prospecting, partner-led growth, channel management, demos, negotiation, forecasting, and closing. Build territory and partner plans, hit revenue quotas, represent CloudSEK to CISOs, collaborate with engineering, marketing and customer success, and manage onboarding and contract negotiations.

We are seeking a Director/VP of Sales for the USA market to develop sales strategies, lead a team, drive revenue, and build customer relationships in the cybersecurity sector.

Own the production lifecycle for traditional ML and generative AI systems, including deployment pipelines, model registries, monitoring, retraining, governance, and incident response. Build reliable AWS-based infrastructure for LLM and agentic applications, establish observability and cost controls, and create operational standards, runbooks, and documentation. Partner with U.S.-based Data Science, Data Engineering, and Product teams while independently managing production workloads during India operating hours.

Lead backend architecture and engineering: design scalable APIs and services, mentor engineers, write complex code, enforce best practices for databases, testing, observability, and integrate ML/LLM workloads.

Own data quality and root-cause analysis for prediction issues: detect anomalies, trace sources, fix processes, automate QA/reporting with Python and SQL, maintain dashboards, and partner with Data Science, Engineering, Product, and Commercial teams to improve prediction performance and customer-facing analytics.

Build governed Databricks Lakehouse foundations and productionize machine learning and GenAI solutions, including forecasting, anomaly detection, NLP, RAG, and predictive analytics. Develop reliable batch and streaming workflows, CI/CD, monitoring, governance, security, lineage, and semantic models. Collaborate with product, data, platform, and domain teams to deliver measurable customer value while meeting reliability, compliance, cost, and operational objectives.

Analyze e-commerce, finance, growth, and operational performance to deliver actionable insights. Build forecasting and predictive models, evaluate projects and KPIs, support stakeholder decision-making, and foster a data-driven culture to improve business performance across departments.

As a Senior Impact Analyst, you will analyze sustainability challenges, design and manage interventions, and work closely with smallholder communities, emphasizing rigorous research and collaboration.

Design, build, and deploy AI/ML and Generative AI solutions for enterprise use cases. Build and maintain end-to-end ML pipelines, deploy models to production, and implement monitoring, observability, and model-drift detection. Work with APIs, vector databases, embeddings, and RAG-based applications, and collaborate with cross-functional teams to optimize scalable AI deployments.

Build and deploy production-grade AI/ML systems spanning LLMs, RAG, agentic AI, computer vision, edge AI, and autonomous robotics. Responsibilities include model fine-tuning, distributed system architecture, GPU optimization, simulation-based robotics development using MuJoCo and Isaac, autonomous driving simulation, and end-to-end algorithm deployment. The role requires translating business problems into scalable AI products and contributing to commercialization and product-market fit.

Architect and improve autonomous AI optimization algorithms for a marketing platform. Analyze A/B tests, integrate AI models, develop multi-touch attribution, expand advertising-channel capabilities, reduce inference costs and latency, incorporate customer feedback, and maintain reliable, continuously improving optimization loops.