AI/ML Engineer Internship (6 Months)

AI/ML Engineer Internship (6 Months) – India

About SvaraAI

SvaraAI is an AI-powered outbound and revenue workspace. We bring everything reps need to go from signal → outreach → meeting in one place:

Leads & Enrichment: consolidate contacts from sheets, forms, LinkedIn, and CRM; de-dupe and score.

AI Personalization: context-aware first lines and replies tuned to your ICP and tone.

Sequencing: email + LinkedIn steps with automatic follow-ups and smart throttling.

Inbox & Analytics: shared conversations, pipeline health, deliverability, and campaign insights.

Connectors: HubSpot/Salesforce, Google Workspace, webhooks, and export APIs.

We’re replacing 5+ tools with one clean workspace, built for small, focused B2B teams that care about speed, precision, and privacy.

Role Description

We are hiring AI/ML Engineer Interns for a 6-month internship program.

Duration: 6 months

Stipend: First 3 months unpaid, next 3 months paid (performance-based after internal assessment).

Location: Remote (India)

As an AI/ML Engineer Intern at SvaraAI, you will work on building and improving core ML-powered features that directly impact how businesses do outbound sales. You’ll help design, implement, and optimize models for personalization, intent detection, scoring, and conversational AI — working closely with our engineering and product teams.

Tech Stack Required

Languages & Libraries: Python, PyTorch/TensorFlow, Scikit-learn, LightGBM, Hugging Face Transformers

Data Handling: Pandas, NumPy, SQL, feature engineering, dataset curation/labeling

ML/AI Concepts: NLP (classification, summarization, embeddings, sentiment), supervised/unsupervised learning, anomaly detection, heuristics + ML hybrids

Deployment Tools:

Good to Have

What You’ll Work On (Summary)

As an AI/ML Engineer Intern at SvaraAI, you’ll get hands-on experience designing and deploying ML models for personalization, lead scoring, and conversational intelligence.

Data Foundations: Data audit & labeling plan, intent features, training set prep

Early Models: Scoring v0 (rule-based), reply classifier (fine-tune), baseline heuristics

Generative AI: First-line generator prompts v1, assistive reply drafts, summarization & sentiment analysis of threads

ML Advancements: Scoring v1 (LightGBM/logit), re-rank personalization, anomaly detection in delivery

Lead Intelligence: Lead routing model v1, cold-start heuristics v2

Prompt & Safety Systems: Prompt library evaluations, guardrails for reliability

You’ll collaborate with the team to ship models and AI-driven features that power personalization, engagement, and automation for real-world SaaS users.

Coding & Research Principles We Follow

KISS (Keep It Simple, Stupid) – start simple, improve iteratively.

YAGNI (You Aren’t Gonna Need It) – avoid premature complexity.

DRY (Don’t Repeat Yourself) – reusable pipelines & utilities.

Reproducibility – experiments tracked, results repeatable.

Test Early, Test Often – validate assumptions with metrics & benchmarks.

Qualifications

Strong understanding of Machine Learning and NLP fundamentals.

Hands-on experience with Python ML ecosystem (scikit-learn, PyTorch/TensorFlow, Hugging Face).

Familiarity with classification, regression, embeddings, and text generation models.

Knowledge of data preprocessing, labeling, and feature engineering.

Good problem-solving and debugging skills.

Strong teamwork and communication abilities.

Currently pursuing or recently completed a Bachelor’s/Master’s in Computer Science, AI/ML, or related field.

Previous ML projects, internships, or GitHub contributions are a big plus.

Hiring Process

Application: Selected candidates will be contacted via LinkedIn message with a Google Form.

Task Round: A time-limited ML/NLP coding task will be assigned. Submissions must be on time.

Interview: Shortlisted candidates will be invited for an interview with our engineering team.

Onboarding: Successful candidates will begin the internship program.

What Success Looks Like

Building scalable, well-documented ML pipelines.

Delivering production-ready models that improve personalization & reply rates.

Writing clean, efficient, and maintainable code.

Collaborating effectively with engineers & product teams.

Learning advanced ML techniques while shipping real impact.

If you’re passionate about AI/ML, NLP, and LLM-powered applications, excited to apply your skills to real-world SaaS challenges, and want to sharpen your expertise in building production ML systems, we’d love to hear from you!