Fractics is hiring for a FRESHER entry level Data Science and Machine Learning Engineer Intern role in India

Position:
• Data Science and Machine Learning Engineer Intern
• Entry level role designed for fresh graduates and early career learners
• Hands on engineering focused internship with real world AI systems
• Exposure to production grade artificial intelligence solutions
• Opportunity to work across data science, machine learning, and applied AI engineering

Company:
• Fractics
• A technology focused consulting and execution firm
• Works on enterprise AI systems and applied artificial intelligence platforms
• Operates in the technology, information, and internet services sector
• Focused on scalable AI driven solutions for business automation and decision systems

Location:
• Gurugram, Haryana, India
• One of the key technology and startup hubs in North India
• Offers access to a strong professional tech ecosystem
• Proximity to enterprise clients and advanced product teams

Job type:
• Full time internship
• Structured as a core development role
• Long term engagement focused on performance and skill growth
• Designed to prepare candidates for full time AI engineering positions

Job mode:
• Hybrid
• Combination of office based and remote work
• Flexibility to collaborate both on site and virtually
• Regular interaction with core engineering and AI teams

Job requisition id:
• FRAC DSML 2025 01

Years of experience:
• 0 to 3 years
• Open to freshers
• Suitable for final year students
• Suitable for early stage professionals looking to transition into AI roles

---

Company description

• Fractics operates as a next generation consulting and execution firm centered around artificial intelligence
• The organization focuses on building proprietary AI products and tailored enterprise solutions
• It serves businesses that aim to use artificial intelligence for scale, automation, and performance growth
• The company applies first principles thinking and outcome driven delivery across all projects
• Fractics supports enterprises from strategy formulation to real world AI deployment
• Its work spans across AI strategy, digital transformation, and intelligent system design
• It actively builds AI powered copilots, contextual agents, and generative AI applications
• The company delivers advanced data and decision systems that include RAG pipelines, vector databases, and large language model operations
• It supports organizations with AI driven marketing, operations, and customer experience optimization
• Fractics also addresses AI governance, compliance, and responsible deployment practices
• It helps organizations prepare for AI adoption through upskilling and modern organizational design
• The company operates with a clear focus on enterprise grade reliability and system performance
• Teams at Fractics work across strategy, research, engineering, data systems, and automation
• It serves clients that want long term transformation instead of short term experimentation
• The company is structured to move fast from idea to implementation
• Fractics promotes an engineering culture centered on problem solving and system thinking
• It encourages innovation in applied AI, automation, and data platforms
• The organization works with growing enterprises that seek scalable digital intelligence systems
• Its mission is to help businesses rethink how they operate, decide, and scale using artificial intelligence
• Fractics positions itself as a builder of real world AI systems, not just proof of concept tools

---

Profile overview

• This internship role is created for individuals who want to build real world machine learning and AI systems
• The intern will work on production grade Agentic RAG and large language model based applications
• The work involves building systems used by real enterprise clients
• The intern will be involved in the entire lifecycle of AI system development
• This includes data preparation, pipeline design, experimentation, testing, deployment, and monitoring
• The role provides deep exposure to large language models, embeddings, vector databases, and AI orchestration
• The intern will design and enhance intelligent retrieval systems using agent based architectures
• They will collaborate with backend engineers, ML engineers, and automation specialists
• The work includes hands on implementation of modern research in applied AI
• The intern will explore and build intelligent workflows powered by large language models
• The role requires regular experimentation to improve system accuracy, speed, and reliability
• The intern will actively participate in improving grounding and reducing hallucination in AI systems
• The candidate will work on real customer data under enterprise quality standards
• This is not a simulation based or academic style internship
• The intern will be accountable for real system performance outcomes
• They will help build, test, and deploy APIs and AI services into live environments
• The role encourages continuous learning and iterative development
• The candidate is expected to take ownership of assigned modules
• The work will involve both independent experimentation and structured team collaboration
• The role develops strong foundations in AI engineering, not only data science theory
• It prepares the intern for future full time AI engineering roles
• The intern will receive direct exposure to modern AI production systems
• The role blends research thinking with real time engineering delivery
• It strengthens problem solving skills in applied machine learning and generative AI systems
• The intern will be trained to move from idea to implementation quickly and responsibly

---

What you will work on

• Designing and refining Agentic RAG pipelines for intelligent information retrieval
• Working on data chunking strategies for large document processing
• Building and optimizing vector embeddings for semantic search
• Improving retrieval and reranking logic for higher accuracy responses
• Cleaning and structuring enterprise datasets for AI workflows
• Enriching raw business data to make it usable for machine learning applications
• Reading, analyzing, and implementing research papers related to modern AI systems
• Converting research ideas into practical engineering solutions
• Building large language model workflows for real world automation use cases
• Creating evaluators to measure system accuracy and performance
• Developing orchestration logic to manage AI agents and tools
• Working with vector databases such as Pinecone, Chroma, and MongoDB Atlas Search
• Conducting experiments on different embedding strategies
• Exploring transformers and modern NLP architectures
• Implementing semantic search capabilities across enterprise datasets
• Deploying machine learning components using FastAPI
• Packaging AI services using Docker for cloud deployment
• Working in cloud environments for system hosting and scalability
• Running controlled experiments to improve system grounding
• Designing workflows to reduce hallucination in language model responses
• Optimizing inference latency for faster system performance
• Testing system outputs against real user queries
• Exploring modern recommender systems integrated with agent based AI
• Building intelligent suggestions and recommendation pipelines
• Experimenting with personalization logic using AI agents
• Improving system reliability through logging and monitoring
• Participating in code reviews and peer learning
• Collaborating with full stack engineers and automation teams
• Supporting backend services that interact with AI pipelines
• Building reusable AI components and microservices
• Maintaining structured documentation for workflows and models
• Applying version control and collaborative coding practices
• Using development tools to improve code quality and review
• Validating AI system outputs using test data and production scenarios
• Enhancing AI system interpretability and result explanation
• Participating in sprint planning and technical discussions
• Learning cloud deployment best practices through real projects
• Gaining exposure to enterprise grade system architecture
• Implementing modular design patterns for scalable AI systems

---

What we are looking for

• Strong interest in machine learning, natural language processing, and large language models
• Solid foundation in Python programming
• Familiarity with common machine learning and NLP libraries
• Exposure to HuggingFace, Scikit Learn, PyTorch, TensorFlow, or related frameworks
• Understanding of embeddings and tokenization concepts
• Basic knowledge of vector search fundamentals
• Exposure to retrieval augmented generation workflows
• Familiarity with frameworks such as LangChain or LlamaIndex through projects or coursework
• Curiosity to understand the full stack of machine learning engineering
• Willingness to learn data processing, modeling, evaluation, and deployment
• Ability to experiment, test, and iterate on AI solutions
• Comfort with debugging data pipelines and ML workflows
• Willingness to read technical documentation and research material
• Ability to translate ideas into working code
• Open mindset toward continuous learning
• Interest in working with real enterprise scale datasets
• Willingness to collaborate across teams and disciplines
• Ability to communicate technical progress clearly
• Structured approach to problem solving
• Basic understanding of API based systems
• Willingness to work in cloud based development environments
• Comfort using version control systems
• Ability to accept feedback and iterate quickly
• Interest in deploying AI solutions into live environments
• Willingness to work in fast paced product teams
• Commitment to ethical and responsible AI practices

---

Bonus points for

• Experience working with FastAPI or backend development fundamentals
• Understanding of knowledge graphs and semantic data modeling
• Familiarity with Docker and basic DevOps concepts
• Ability to work with containerized applications
• Exposure to system deployment practices
• Understanding of cloud infrastructure basics
• Interest in building scalable distributed systems
• Experience with GitHub based collaboration
• Familiarity with AI assisted development tools
• Comfort with self review and performance iteration
• Independent mindset toward improving system quality
• Willingness to adopt new tools and workflows quickly

---

What you will get

• Hands on experience building enterprise grade Agentic AI systems
• Direct exposure to production deployment environments
• Opportunity to contribute to live customer facing solutions
• Structured mentorship from machine learning and backend engineers
• Learning support from experienced automation specialists
• Ownership of real technical modules
• Responsibility for system performance and reliability
• A fast paced environment focused on applied learning
• Continuous feedback on technical progress
• Growth focused engineering culture
• Opportunity to convert to a full time role based on performance
• Strong resume building exposure in AI engineering
• Practical experience in generative AI applications
• Training in system thinking and full lifecycle AI development
• Confidence in deploying machine learning solutions to production
• Long term career value in applied artificial intelligence

---

Qualifications

• Bachelor degree in computer science, data science, artificial intelligence, or related technical fields
• Final year students may apply
• Recent graduates may apply
• Strong academic interest in machine learning and AI systems
• Working knowledge of Python programming
• Basic understanding of data structures and algorithms
• Familiarity with statistics and probability
• Exposure to supervised and unsupervised learning methods
• Basic understanding of neural networks
• Awareness of NLP concepts
• Coursework or projects related to data science or AI
• Participation in hackathons or technical competitions preferred
• Ability to demonstrate self learning through projects
• Familiarity with online coding platforms or portfolios
• Willingness to maintain documentation and reports
• Professional attitude toward learning and collaboration
• Interest in enterprise grade system engineering
• Willingness to work under technical mentorship
• Ability to manage time and deliver assigned tasks
• Comfort working with ambiguity in early stage product development
• Strong motivation to build real AI systems

---

Additional info

• This role follows a hybrid working structure
• Interns may be required to visit the office for collaboration and reviews
• Work hours follow a full time schedule
• Performance based conversion to a permanent role may be offered
• The internship is focused on production systems, not classroom training
• Interns will work under supervision of experienced engineers
• Evaluation is based on learning speed, quality of output, and ownership
• Interns are expected to maintain confidentiality of enterprise data
• Adherence to responsible AI practices is mandatory
• Collaboration with cross functional teams is a core part of the role
• Exposure to client projects may be provided
• Interns may participate in internal innovation initiatives
• Learning resources and technical guidance will be provided
• Career guidance and growth direction will be supported
• Long term association may be explored based on mutual interest

Please click here to apply.