Top Machine Learning Jobs in Hyderabad

Talent community opportunity for candidates interested in future roles across engineering, AI and machine learning, data, product, finance, customer success, sales, marketing, operations, and internships. MeeruAI seeks curious, collaborative problem-solvers with relevant education, experience, or demonstrated skills, plus interest in AI, technology, finance, accounting, or enterprise software. This is not an application for a specific open position.

Own the administration, security, reliability, observability, and cost management of the company’s data platform. Operate and optimize Snowflake during migration from BigQuery, manage AWS S3 and vendor data ingestion, extend practices to Postgres and NoSQL databases, and implement access controls, SSO, encryption, monitoring, incident response, infrastructure as code, and CI/CD. Support machine learning and AI workloads, manage data vendors and tooling, improve cost transparency, and establish reusable data operations practices across teams.

Own strategic AI-native and enterprise accounts across the full sales cycle, from pipeline generation and prospecting through closing and expansion. Partner with Solutions Architecture, Engineering, and Research teams to develop technical solutions and proof-of-concept evaluations for inference and post-training workloads. Build executive and technical relationships, manage complex sales cycles, identify customer use cases, inform product strategy, and improve sales processes.

ServiceNow ITOM Engineer responsible for configuring and administering ITOM modules, managing ServiceNow application lifecycles, supporting incidents, troubleshooting performance issues, and applying ITIL practices. The role also develops Generative AI and Agentic AI solutions, creates prompts, evaluates AI outputs, and automates operational workflows. The engineer collaborates with technical teams and business stakeholders to improve infrastructure visibility, service availability, incident resolution, and application support.

Design, develop, and deploy scalable AI-powered applications using Python, Azure cloud services, and machine learning models. Build and optimize Microsoft SQL Server databases, integrate Azure infrastructure services, support .NET systems, and develop responsive interfaces with Angular, React, and JavaScript. Responsibilities include architecture, performance tuning, testing, debugging, reliability improvements, and production deployment of intelligent applications.

Lead cybersecurity trials and proof-of-concept engagements, translating technical findings into business value for executives and security teams. Research US regional cyber threats, create intelligence briefs and exposure reports, support partners with onboarding and co-selling, and contribute to thought leadership through events, webinars, workshops, and research-driven content. The role requires strong threat intelligence, offensive security, consulting, communication, presentation, collaboration, and self-direction skills.

Develop, test, deploy, and support production-ready machine learning solutions. Responsibilities include data preparation, exploratory analysis, baseline modeling, feature engineering, model evaluation, Python development, APIs, pipelines, containerization, MLOps, testing, monitoring, documentation, and responsible AI risk identification. The role supports classification, regression, clustering, forecasting, NLP, and generative AI use cases in a collaborative Agile environment.

Drive adoption and value realization of Neara’s digital-twin platform among Japanese utility customers. Responsibilities include customer engagement, platform enablement, training, ROI and business-case development, stakeholder management, solution expansion, and collaboration with Sales, Deployment, Product, and Engineering teams. The role requires translating customer strategic goals into technical solutions and measurable business outcomes, with regular customer and team travel in Japan and internationally.

Build and deploy engineering-grade digital models for Japanese utility clients by integrating GIS, LiDAR, and other data sources. Configure platform projects, create dashboards, resolve model inaccuracies, support technical delivery and pre-sales, and collaborate with product and engineering teams on platform improvements and Japanese localization. The role requires customer-facing communication in Japanese, global collaboration in English, mentoring, and occasional travel to customers and international offices.

Design, clean, analyze, and visualize data using SQL and Python; build Tableau dashboards; apply statistics and basic ML/DS concepts; follow software/data-engineering practices (version control, unit testing, high-performance code); work with structured and unstructured data and cloud environments.

Lead product vision and discovery for a genomics-driven diagnostic and personalization platform. Validate hypotheses with patients, clinicians, and partners; translate research into product specs; coordinate labs and data providers; partner with engineers and researchers to build AI prototypes; shape data, regulatory, and GTM strategy while making core product and architectural decisions.

Build core components of a diagnostic and personalization platform: backend data pipelines, frontends, services for genomic data ingestion, ML model integration, APIs, and cloud infrastructure. Collaborate with product and ML teams, iterate on prototypes and MVPs, and make architectural decisions supporting scalability and reliability.

Build production AI systems for finance teams, including per-customer anomaly detection models, grounded natural-language generation, agentic workflows, and managed LLM integrations. Develop Python services with SQL and warehouse access in containerized customer deployments. Establish rigorous evaluation, anti-leakage, hallucination, grounding, and faithfulness controls, incorporate human confirmation checkpoints, and convert user feedback into durable model and system improvements.

Own core backend services for a finance AI platform, building reliable, configuration-driven computation systems over financial and operational data. Ensure results are traceable, reproducible, auditable, and correctly reconciled. Provide technical leadership through architecture, design reviews, engineering standards, and mentorship while partnering with data, AI, and product teams. The role involves production Python, advanced SQL and data modeling, multi-tenant or customer-hosted deployments, and high standards for correctness and system reliability.

Develop generative AI applications, chatbots, intelligent agents, RAG pipelines, semantic search systems, and enterprise automation solutions. Build Python backend services and REST APIs, integrate commercial and open-source language models, use AI development frameworks, support AWS deployment, and perform prompt engineering, testing, debugging, and optimization. Collaborate with engineering and product teams to deliver maintainable, production-ready AI software.

Develop and maintain scalable backend services, APIs, and Kubernetes environments supporting AI and machine learning model deployment. Responsibilities include system architecture, distributed computing, asynchronous processing, data and model lifecycle management, cloud and serverless deployment, testing, monitoring, security, and production readiness. The role collaborates with machine learning teams to transition solutions from proof of concept to reliable production systems while applying DevOps and continuous improvement practices.

Build and deploy production-grade machine learning, deep learning, and Generative AI solutions. Responsibilities include developing models, RAG pipelines, AI agents, prompt engineering, data and training pipelines, scalable APIs, and MLOps practices. The role evaluates and optimizes model performance, cost, and latency while collaborating with product, data, and engineering teams to move AI capabilities from proof of concept to production.

Conduct experimental research in machine intelligence, develop and refine machine learning algorithms and models, apply research findings to existing AI systems, collaborate with engineers and researchers, publish results, and stay current with advances in machine learning.

Designs, develops, deploys, and maintains production-grade AI/ML solutions using deep learning, neural networks, AWS, and Amazon SageMaker. Responsibilities include building ML pipelines, developing chatbots and conversational AI, applying computer vision to image data, monitoring deployed models, and collaborating with teams to translate business needs into AI strategies. Requires 8–12 years of experience and expert-level AWS and machine learning expertise.

Write technically accurate case studies, articles, product explainers, launch materials, and social content for AI engineers, researchers, and technical leaders. Interview customers, partners, and internal experts; learn HUD’s platform and workflows; explain technical products and research clearly; support founder communications; manage content from ideation through publication; and improve content using reader and go-to-market feedback.

Develop, train, evaluate, and deploy small task-specific ML models for real-time English conversational skills (ASR, pronunciation, grammar, recommendations). Create datasets and task-specific evaluations, improve internal SOTA, mentor engineers, and contribute to hiring.

Develop AI-powered product features for local discovery, including personalized recommendations, content curation, search, and ranking. Build data pipelines by scraping, cleaning, and structuring datasets for machine learning models. Collaborate with product, design, and engineering teams to integrate AI into the platform, while continuously improving machine learning capabilities and user experiences.

Seeking senior software engineers with proven expertise in at least one technical area, including AI/ML, LLMs, data, full-stack or backend development, automation, cloud infrastructure, and DevOps. Responsibilities include solving complex problems independently, collaborating across technologies, and delivering production-quality projects in a startup environment. Engagements may be project-based or ongoing.

The Customer Success Business Partner at Lyric drives customer adoption and retention for strategic accounts, leveraging expertise in AI and supply chain operations.

Develop, validate, debug, and optimize machine learning models, training pipelines, inference systems, and numerical implementations using Python. Build reproducible workflows, diagnose computational issues, create systematic tests, and improve memory usage, throughput, and performance. Review code and solutions, use coding agents in development, document technical findings, and deliver reliable work independently within project deadlines.