ai/ml intern (machine learning & generative ai)

applied ai researcher (optimization & domain models)

data scientist / ai engineer / analytics engineer

(immediate joiner) mern/full stack developers

agentic ai engineer

ai / ml engineer

ai/ml intern (machine learning & generative ai) Jobs in India

applied ai researcher (optimization & domain models) Jobs in India

data scientist / ai engineer / analytics engineer Jobs in India

applied ai researcher optimization domain models

veltris

<div><div><h3 class="jobSectionHeader">Job Information</h3>
<ul><div>Work Experience
<div>15+ Years
</div></div><div>Date Opened
<div>06/12/2026
</div></div><div>Industry
<div>IT Services
</div></div><div>Job Type
<div>Staff Aug Consultant
</div></div><div>Remote Job
</div></ul></div><div><h3 class="jobSectionHeader">Job Description</h3>
<div>This is a remote position.
Role Type: Research-to-Production Specialist Focus: Domain-Specific Models, Optimization &amp; Safety Reports to: Chief AI Evangelist &amp; Product Head 
Role Summary 
The Applied AI Researcher (Optimization &amp; Domain Models) is responsible for adapting and optimizing AI models for micro-industry-specific problems. This role bridges theoretical rigor and real-world deployment, ensuring models are not only accurate but safe, explainable, and production-ready. 
You will own problem-specific reasoning models, vertical LLM tuning, anomaly detection, and quality models that power defensible SaaS offerings. 
Key Responsibilities 
Domain Model Development<ul><li> 
Design problem-specific reasoning and optimization models.</li><li> 
Fine-tune vertical LLMs for industry-specific language, workflows, and constraints.</li><li> 
Build anomaly detection, prediction, and quality inspection models.</li><li> 
Adapt foundation models to operate under domain rules, policies, and regulations.</li></ul> 
Evaluation, Guardrails &amp; Safety<ul><li> 
Own evaluation loops (offline, online, human-in-the-loop).</li><li> 
Design guardrails for hallucination control, bias mitigation, and policy compliance.</li><li> 
Implement safety tooling for enterprise-grade AI deployments.</li><li> 
Define success metrics tied to business and operational outcomes.</li></ul> 
Research to Production<ul><li> 
Convert research prototypes into deployable, scalable micro-industry models.</li><li> 
Partner with engineers to integrate models into agents and SaaS workflows.</li><li> 
Document model behavior, assumptions, and failure modes.</li><li> 
Create repeatable model adaptation playbooks.</li></ul> 
IP &amp; Thought Leadership<ul><li> 
Contribute to proprietary model architectures and training strategies.</li><li> 
Publish internal whitepapers and external POVs where appropriate.</li><li> 
Support GTM narratives with credible technical depth.</li></ul> 
Required Qualifications<ul><li> 
Desirable PhD in AI, ML, Applied Mathematics, Operations Research, or related field.</li><li> 
Strong background in optimization, probabilistic modeling, or deep learning.</li><li> 
Experience fine-tuning LLMs or training domain-specific models.</li><li> 
Hands-on experience with Python, PyTorch, TensorFlow, or JAX.</li></ul> 
Preferred Qualifications<ul><li> 
Experience with enterprise or regulated domains (healthcare, finance, telecom).</li><li> 
Familiarity with reinforcement learning or constrained optimization.</li><li> 
Exposure to safety, alignment, or AI governance frameworks.</li></ul> 
Success Metrics<ul><li> 
Deliver 1 domain-tuned model per quarter.</li><li> 
Demonstrate measurable performance lift vs baseline models.</li><li> 
Deploy models into at least 2 production workflows.</li><li> 
Reduce inference errors or quality issues by 25%.</li></ul></div></div></div>

Applied AI Researcher (Optimization & Domain Models)

Great Learning

<div><div>Job Description
<ul><li>Own analytics and AI solutions end-to-end: diagnose the root cause, build the solution, and follow it through to a business decision and measurable outcome.</li><li>Develop data and AI-powered solutions &mdash; predictive models, automated pipelines, intelligence layers &mdash; that drive real outcomes and reduce the need for manual intervention.</li><li>Build and maintain scalable, reliable data infrastructure, including robust data pipelines, foundational data models, and comprehensive data quality checks that the wider team can trust and build upon.</li><li>Build and optimize the analytics intelligence layer to ensure the automated, scheduled delivery of granular insights to the appropriate stakeholders.</li></ul> 
</div><div>Qualifications 
<div> 
<ul><li>1+ years of applied experience working on analytics or data science projects</li><li>Advanced problem-solving capabilities, with a track record of navigating ambiguous questions to identify root causes rather than simply executing requested queries.</li><li>Strong Python and SQL, and solid data-management fundamentals.</li><li>Baseline AI fluency, including active use of LLMs for workflow optimization and hands-on experience building AI-powered operational workflows.</li><li>Intrinsically motivated problem solvers who demonstrate ownership and genuinely enjoy learning</li><li>The ability to work independently &mdash; you can take a problem, scope it, and run with it without being handed every step.</li></ul></div></div></div>

Data Scientist / AI Engineer / Analytics Engineer

NIIT Foundation

Job description:Role OverviewThe Placement Coordinator &ndash; STEM is responsible for connecting students or trainees with employment opportunities in science and technology fields. This role focuses on aligning candidate skills with industry requirements in areas such as software development, data science, engineering, research, and emerging technologies.Key Responsibilities 1. Industry Engagement &amp; Partnerships<ul><li>Build and maintain relationships with IT companies, engineering firms, R&amp;D organizations, and startups</li><li>Identify hiring needs in domains like AI, data science, software engineering, electronics, and mechanical fields</li><li>Coordinate campus recruitment drives and industry interactions</li></ul>2. Candidate Skill Assessment &amp; Alignment<ul><li>Evaluate students&rsquo; technical skills, projects, and certifications</li><li>Match candidates with suitable job roles (developer, analyst, engineer, researcher, etc.)</li><li>Identify skill gaps and collaborate with trainers/faculty to address them</li></ul>3. Pre-Placement Training &amp; Readiness<ul><li>Organize technical training sessions (coding, tools, domain-specific skills)</li><li>Conduct mock interviews (technical + HR) and aptitude tests</li><li>Prepare students for assessments on platforms like HackerRank and LeetCode</li></ul>4. Placement Coordination<ul><li>Plan and execute placement drives, hackathons, and recruitment events</li><li>Coordinate interview schedules, technical tests, and hiring processes</li><li>Ensure candidates are interview-ready and meet employer expectations</li></ul>5. Internship &amp; Research Opportunities<ul><li>Facilitate internships with tech companies, startups, and research labs</li><li>Connect students with live projects, fellowships, and innovation programs</li><li>Encourage participation in hackathons and technical competitions</li></ul>6. Data Management &amp; Reporting<ul><li>Maintain databases of student profiles, technical skills, and placement records</li><li>Track KPIs such as placement rate, average salary, and domain-wise hiring</li><li>Generate reports and insights to improve placement strategies</li></ul>7. Post-Placement Support<ul><li>Monitor candidate performance and retention in technical roles</li><li>Collect feedback from employers regarding skill relevance</li><li>Support career progression through upskilling and certifications</li></ul>8. Academic &amp; Training Collaboration<ul><li>Work with faculty to align curriculum with industry standards</li><li>Recommend inclusion of emerging technologies (AI, cloud, cybersecurity, IoT)</li><li>Support development of industry-relevant projects and capstone work</li></ul>Required Qualifications<ul><li>Bachelor&rsquo;s degree in Engineering, Computer Science, IT, or related STEM field</li><li>1&ndash;3 years of experience in placements, recruitment, or training (preferred)</li></ul>Key Skills &amp; Competencies<ul><li>Basic understanding of STEM domains and technical roles</li><li>Strong communication and employer engagement skills</li><li>Knowledge of technical hiring processes and assessments</li><li>Analytical thinking and problem-solving</li><li>Organizational and coordination skills</li></ul>Preferred Attributes<ul><li>Experience in campus placements or EdTech/STEM programs</li><li>Familiarity with coding platforms and technical interview processes</li><li>Passion for technology and career development</li></ul>Key Performance Indicators (KPIs)<ul><li>Number of students placed in STEM roles</li><li>Average salary/package and job relevance</li><li>Internship conversion rate (internship &rarr; full-time)</li><li>Employer satisfaction and repeat hiring</li><li>Student retention and career progression</li></ul>Pay: ₹32,000.00 - ₹40,000.00 per monthExperience:<ul><li>IT Placement: 1 year (Preferred)</li><li>Placement: 2 years (Required)</li><li>NGO/CSR: 1 year (Required)</li></ul>Work Location: In person

Placement Coordinator (IT)

Fractal Analytics

<div>It's fun to work in a company where people truly BELIEVE in what they are doing!
We're committed to bringing passion and customer focus to the business.<div><div>
Job Description</div></div><div><div><div><div><div><div><div><div><div><h1 class="jobSectionHeader">
EL3 &ndash; Databricks MLOps Engineer (Contract)</h1>
Domain: Claims Payment Integrity | M&amp;R, C&amp;S, E&amp;I Claims (preferred) 
Actuarial &amp; Forecasting Analytics Exposure is an Added Advantage 
Tech Stack: Databricks, Spark, Python, Scala, Azure, GitHub Actions, Terraform 
AI/LLM Capabilities: Embedding Models, LLM Integration, LangChain Agentic Frameworks<h2 class="jobSectionHeader">
Role Summary</h2>
The EL3 Databricks MLOps Engineer is a senior hands-on role responsible for enabling end-to-end machine learning lifecycle automation on Databricks. This includes building and maintaining the CI/CD infrastructure, environment configuration, packaging and deploying ML models, supporting reproducible experiments, and ensuring scalable job orchestration for AI/ML workloads, including LLM-based applications.
The role partners closely with Data Scientists, AI/ML Engineers, platform teams, and business stakeholders within Claims Payment Integrity to ensure robust, reliable, and automated ML delivery.<h2 class="jobSectionHeader">
Key Responsibilities</h2><ul><li>
Enable and automate the end-to-end ML lifecycle on Databricks (environment setup, model workflow automation, job scheduling, monitoring hooks).</li><li>
Build frameworks, templates, and utilities that make ML development and experimentation reproducible and scalable.</li><li>
Implement CI/CD pipelines using Git, GitHub Actions, Jenkins, Azure DevOps, or similar tools.</li><li>
Package, version, and deploy ML models into Databricks-managed execution environments.</li><li>
Set up automated workflows for training, retraining, evaluation, and scheduled job execution.</li><li>
Support creation and integration of machine learning models including classification, forecasting, anomaly detection, NLP, and PI models.</li><li>
Enable LLM/GenAI-driven solutions by integrating:<ul><li>
Embedding model generation</li><li>
RAG architectures</li><li>
Vector databases</li><li>
LangChain agentic workflows</li></ul></li><li>
Optimize resource usage, runtime configurations, and code execution patterns for ML workloads.</li><li>
Collaborate with Data Scientists to translate experimental notebooks into production-ready pipelines.</li><li>
Implement platform-level controls for environment consistency, dependency management, access control, and model versioning.</li><li>
Support troubleshooting, debugging, and performance improvements for ML workloads.</li><li>
Document standards, templates, guidelines, and best practices for MLOps teams.</li><li>
Work cross-functionally with product, engineering, and analytics teams across PI.</li></ul><h2 class="jobSectionHeader">
Required Qualifications</h2><ul><li>
Bachelor&rsquo;s/Master&rsquo;s degree in Computer Science, Engineering, or related field</li><li>
6&ndash;9 years of relevant experience in ML Engineering, MLOps, or platform engineering</li><li>
Strong hands-on experience with Databricks, Spark (batch/streaming), Python, Scala</li><li>
Experience enabling ML lifecycle tools such as MLflow (tracking, packaging, model registration)</li><li>
Strong CI/CD experience using Git, GitHub Actions, Jenkins, or Azure DevOps</li><li>
Experience deploying AI/ML models into cloud environments (Azure preferred)</li><li>
Ability to create and integrate embedding models, semantic vectors, and LLM-driven components</li><li>
Experience with LangChain for agentic workflows and integration of tools/functions</li><li>
Strong problem-solving, debugging, and collaboration skills</li></ul><h2 class="jobSectionHeader">
Preferred Qualifications</h2><ul><li>
Experience with Azure OpenAI or OpenAI-compatible LLM APIs</li><li>
Familiarity with healthcare claims workflows, PI, FWA, provider billing, or pricing</li><li>
Experience in Agile/Scrum environments</li><li>
Strong understanding of software engineering best practices, packaging, dependency management</li></ul><h2 class="jobSectionHeader">
Good-to-Have Data Knowledge</h2><ul><li>
Call Center datasets (member &amp; provider interactions)</li><li>
Provider RCM datasets (billing, coding, authorizations)</li><li>
EHR/clinical datasets for cross-domain validation</li></ul></div></div></div></div></div></div></div></div></div>
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!<h3 class="jobSectionHeader">
Hiring Related Queries</h3>
India: HiringsupportIndia@fractal.ai
Outside India: HiringsupportROW@fractal.ai
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MLOps Engineer

Sitero LLC

<div>Sitero is seeking a Senior AI Developer to design, build, and deploy next-generation AI-driven applications. You will bridge the gap between advanced machine learning models and scalable, production-ready enterprise solutions leveraging modern cloud AI platforms. 
Key Responsibilities
<ul>
<li>Application Architecture: Design robust, scalable architectures for AI-powered applications natively on the cloud.</li>
<li>Model Orchestration: Deploy and manage foundation models using AWS Bedrock and orchestrate complex prompt workflows.</li>
<li>API Development: Build and maintain secure, high-performance APIs and microservices using Python, Java, or Node.js.</li>
<li>Data Integration: Implement Retrieval-Augmented Generation (RAG) pipelines to connect internal corporate data with language models.</li>
<li>System Optimization: Optimize application latency, token throughput, and overall compute costs for AI inference.</li>
<li>Collaboration &amp; Mentorship: Partner with product teams to build AI agents and mentor junior engineers on engineering best practices.
</li></ul>Required Skills &amp; Qualifications
<ul>
<li>Education: Bachelor&rsquo;s or master&rsquo;s degree in computer science, Engineering, or a related field.</li>
<li>AI &amp; Cloud Platforms: Deep hands-on experience with AWS Bedrock, including model evaluation and agent setup.</li>
<li>AI Ecosystem: Experience with orchestration frameworks like LangChain, LlamaIndex, or the Hugging Face ecosystem.</li>
<li>Core Development: Strong software engineering proficiency in Python, Java, or Node.js.</li>
<li>Data Systems: Experience working with Vector Databases (e.g., Pinecone, Amazon OpenSearch, Milvus) and SQL/NoSQL systems.</li>
<li>Software Engineering: Solid understanding of CI/CD pipelines, Docker, Kubernetes, and Git workflows.
</li></ul>Preferred Qualifications
<ul>
<li>Experience with AWS Certified Generative AI Developer credentials or building production-grade enterprise agents.</li>
<li>Familiarity with advanced prompt optimization, guardrails, and model fine-tuning techniques.</li>
<li>5-8 years experience</li></ul></div>

Senior AI Developer

Better Analytics Private Limited

We are looking for a motivated AI/ML Intern to join our team and work on real-world machine learning and generative AI projects. This internship offers hands-on experience in building, training, and deploying AI models while collaborating with experienced engineers and product teams.Key Responsibilities<ul><li>Assist in developing and implementing machine learning models.</li><li>Work with structured and unstructured datasets for data preprocessing and analysis.</li><li>Build and evaluate predictive models using Python and popular ML libraries.</li><li>Develop AI-powered applications using Large Language Models (LLMs) and prompt engineering.</li><li>Experiment with Retrieval-Augmented Generation (RAG) and vector databases.</li><li>Support model deployment, testing, and performance optimization.</li><li>Research emerging AI technologies and contribute innovative ideas.</li><li>Document experiments, findings, and technical implementations.</li></ul>Required Skills<ul><li>Currently pursuing or recently completed a Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.</li><li>Strong programming skills in Python.</li><li>Basic understanding of machine learning concepts and algorithms.</li><li>Familiarity with libraries such as Scikit-learn, Pandas, NumPy, TensorFlow, or PyTorch.</li><li>Knowledge of SQL and data analysis.</li><li>Understanding of Generative AI, LLMs, or prompt engineering is a plus.</li><li>Strong analytical and problem-solving skills.</li><li>Good communication and teamwork abilities.</li></ul>Preferred Qualifications<ul><li>Experience with LangChain, LlamaIndex, or similar AI frameworks.</li><li>Familiarity with APIs, REST services, or FastAPI.</li><li>Knowledge of Git and version control.</li><li>Experience with cloud platforms (AWS, Azure, or GCP) is a plus.</li><li>Personal, academic, or open-source AI/ML projects are highly valued.</li></ul>Internship Details<ul><li>Employment Type: Internship</li><li>Duration: 3&ndash;6 months</li><li>Work Mode: On-site</li></ul>Pay: ₹15,000.00 - ₹20,000.00 per monthWork Location: In person

AI/ML Intern (Machine Learning & Generative AI)

Target

<div>About the Role 
 
Join the Data Analytics (DA) team at TII across domains such as Marketing and Digital, Merchandising, Supply Chain and Logistics, Store Operations, Finance and more. Here you'll collaborate with business leaders to turn data into insights that drive strategic decisions. You&rsquo;ll be part of a fast-moving, high-impact environment focused on leveraging business intelligence and advanced analytics to solve real-world problems. 
This role combines technical expertise with business understanding to uncover and communicate actionable insights using cutting-edge statistical and analytical techniques. 
 
Behind one of the world&rsquo;s best loved brands is a uniquely capable and brilliant team of data scientists, engineers and analysts. The Target Data &amp; Analytics team creates the tools and data products to sustainably educate and enable our business partners to make great data-based decisions at Target. We help develop the technology that personalizes the guest experience, from product recommendations to relevant ad content. We&rsquo;re also the source of the data and analytics behind Target&rsquo;s Internet of Things (iOT) applications, fraud detection, Supply Chain optimization and demand forecasting. We play a key role in identifying the test-and-measure or A/B test opportunities that continuously help Target improve the guest experience, whether they love to shop in stores or at Target.com.
Key Responsibilities: 
 Demonstrate thought leadership in the application of data, analytics, and AI to 
influence merchandising strategy and outcomes 
 Independently translate ambiguous business problems into structured analytical 
approaches, including scenario, decision, and action modelling 
 Partner with Target business stakeholders to understand priorities and roadmaps, 
validate analytical requirements, and present insights and recommendations with 
clarity and impact 
 Design, develop, and deliver analytical and AI-driven solutions (including GenAI, 
Agent, and Agentic approaches) that enable decision support, forecasting, 
optimization, and automation 
 Apply advanced analytics techniques, including causal, predictive, and prescriptive 
analytics, to drive deeper understanding of business levers and inform optimal 
actions 
 Work with large-scale datasets using platforms such as GCP BigQuery, Spark, and 
SQL-based data warehouses; build and maintain reliable data pipelines using Airflow 
or similar orchestration tools 
 Evaluate and monitor AI-driven analytical workflows, defining quality metrics (e.g., 
accuracy, relevance), assessing reliability, and measuring tangible business impact 
 Synthesize complex analyses into compelling narratives; act as a strong storyteller 
who can influence executive and merchant audiences using data 
 Develop strong business acumen and cultivate trusted relationships with internal 
clients and partners 
 Create high-quality analytical and AI solution artifacts, documenting methodologies, 
assumptions, models, and decision logic 
 Contribute to knowledge-sharing systems that support reuse, iteration, and 
continuous improvement of analytical models and approaches 
 Ensure compliance with corporate data protection standards and embed responsible 
AI practices, including data privacy, bias mitigation, and explainability 
 Stay current with industry trends, emerging methodologies, and best practices in 
analytics, AI, and retail decision science</div>

Lead Data Analyst

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