scikit-learn Jobs in Pune.
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The Role
Lead the maturation of Securly's content classification system — building the ML infrastructure that determines, at scale, whether web content is appropriate for K-12 students, and establishing the rigorous evaluation framework that product and leadership teams depend on.
This is applied ML with direct student safety impact — not research. You will lead a significant uplift of Securly's classification models: refactoring binary models to proper multiclass classification, building labeled evaluation datasets, and producing standardized model cards with per-category precision, recall, F1, and confusion matrix analysis.
At L5, you are the technical leader of the data science function for content safety. You will define the evaluation methodology the team follows, set the standard for what a model card must contain before a model ships, mentor the team on applied ML rigor, and serve as the interface between data science and engineering on production integration constraints.
What It Means to Be L5 at Securly
L5 at Securly is a Staff Engineer. You are the technical owner, not just an implementer.
- Drive technical direction for your initiative end-to-end: from architecture to production, with minimal oversight from your engineering manager.
- Identify and resolve ambiguity in requirements, system boundaries, and design tradeoffs without waiting for a fully-formed spec.
- Mentor L3/L4 engineers on the team: code reviews, design feedback, pairing, and raising the bar for what production-quality work looks like.
- Partner with your L6 technical lead and the Distinguished Engineer on architectural decisions, surfacing tradeoffs clearly rather than deferring them upward.
- Contribute to cross-team engineering standards: you are expected to influence practices beyond your immediate squad.
- Translate technical context into clear written artifacts that non-engineers (PM, Support, Leadership) can act on.
- Participate in on-call rotation and own the full incident lifecycle for your system: detection, diagnosis, resolution, and retrospective.
What You'll Do
- Define the evaluation methodology for content classification at Securly: establish what a model card must contain and hold every model release to that standard before it ships.
- Lead the multiclass refactor of Securly's content classification models: redesign binary models to handle multi-label, multi-class content categories (Adult Content, Violence, Self-Harm, Social Media, and others).
- Build and maintain labeled evaluation datasets with robust annotation workflows; address class imbalance and label noise systematically; document dataset curation decisions in a versioned data card.
- Connect offline evaluation to production monitoring — surface classification drift and error patterns before they become customer-facing issues.
- Investigate and resolve misclassification errors: false positives (over-blocking) and false negatives (under-blocking); produce written root cause analyses.
- Build and maintain training data pipelines: ingestion, cleaning, labeling, and versioning at scale.
- Mentor the existing AI team on evaluation methodology, model development practices, and data science communication rigor.
- Communicate precision/recall tradeoffs to product managers and engineers; produce executive-level summaries of classification quality for leadership.
- Collaborate with engineering to integrate model outputs into the production filtering stack with appropriate latency and reliability constraints.
- Research and prototype improvements: feature representations, model architectures, active learning for label efficiency, domain adaptation for emerging content categories.
Skills & Requirements
Must-Have
- Machine learning — multi-label/multi-class classification, model evaluation methodology, handling class imbalance, feature engineering for text and URL data. 5+ years in applied ML roles.
- Python (ML stack) — production-quality code: scikit-learn, PyTorch or TensorFlow, pandas, numpy. Notebooks for exploration; production-grade pipelines for delivery.
- Text / NLP feature engineering — URL tokenization, domain analysis, HTML content features, TF-IDF or embedding-based representations for web content classification.
- ML evaluation rigor — precision/recall tradeoffs, confusion matrix analysis, offline vs. online evaluation, A/B testing, reproducible model cards. At L5, you define the evaluation standard.
- Data engineering for ML — training data pipelines, data versioning, handling noisy and partially labeled datasets, annotation workflow design.
- Technical communication and stakeholder influence — ability to present quantitative model quality findings to both engineering and non-technical leadership.
Strongly Preferred
- Large-scale classification in production — shipping models with latency and throughput constraints; understanding the gap between offline eval metrics and live production behavior.
- Active learning / annotation workflows — strategies for efficient label acquisition on large, imbalanced datasets.
- Cloud ML infrastructure — AWS SageMaker, GCP Vertex AI, or equivalent for training pipelines, experiment tracking, and model deployment.
Nice to Have
- Web content / URL classification domain — prior work on web categorization, safe browsing, or content policy systems.
- K-12 / CIPA compliance — understanding of regulated content categories and compliance requirements around false negative rates.
- LLM-based classification — zero-shot or few-shot content classification for emerging categories without labeled training data.
- Graph / network features — domain co-occurrence, DNS graph signals, or network-based features for domain classification at scale.
Who You Are
- You have shipped ML models to production and lived with the consequences — you know what model drift looks like and how to catch it before it becomes a customer issue.
- You treat evaluation as a first-class engineering artifact. A model without a model card is not finished — and you set and enforce that standard for the team.
- You define the methodology, not just apply it. You produce the evaluation framework that other data scientists use, and you hold them to it.
- You can communicate precision/recall tradeoffs to a product manager and to a senior engineer in the same conversation, calibrated to each audience.
- You are energized by problems with real stakes: a false negative in Self-Harm classification is not an acceptable error rate.
- You mentor by example and by expectation: your code, your analysis, and your documentation set the standard.
About Securly
Securly processes over 1.1 billion requests per day and 54 TB of data daily, protecting more than 20 million students across 20,000+ schools globally. Since pioneering the first cloud-based web filter for K-12 in 2013, Securly has built one of the most trusted, high-scale platforms for student safety, wellness, and engagement. By turning data into meaningful, actionable intelligence, Securly enables schools to identify risk earlier, reduce harmful incidents, and strengthen student support.
We are proud to be consistently recognized as a Top Place to Work, named a Top 40 Most Used EdTech platform, and included on the GSV 150 list as one of the most transformational growth companies in digital learning and workforce skills.
Benefits
- Comprehensive Health Insurance (employee, parents, spouse, children)
- Accidental & Term Life Insurance
- Learning & Development reimbursement
- Paid Time Off
- Public Holidays (10+ per year)
- Retirement Benefits (EPF & gratuity)
- Parental Leave (as per statutory norms)
Securly is an Equal Opportunity Employer committed to inclusion, fairness, and respect. We welcome applicants from all backgrounds, identities, and experiences. #LI-REMOTE #LI-DO1
Ready to apply?
Apply to Securly
The Role
The Senior Data Scientist will lead the maturation of Securly's content classification system — building the ML infrastructure that determines, at scale, whether web content is appropriate for K-12 students, and establishing the rigorous evaluation framework that product and leadership teams depend on.
This is applied ML with direct student safety impact — not research. You will lead a significant uplift of Securly's classification models: refactoring binary models to proper multiclass classification, building labeled evaluation datasets, and producing standardized model cards with per-category precision, recall, F1, and confusion matrix analysis.
At L5, you are the technical leader of the data science function for content safety. You will define the evaluation methodology the team follows, set the standard for what a model card must contain before a model ships, mentor the team on applied ML rigor, and serve as the interface between data science and engineering on production integration constraints.
What It Means to Be L5 at Securly
L5 at Securly is a Staff Engineer. You are the technical owner, not just an implementer.
- Drive technical direction for your initiative end-to-end: from architecture to production, with minimal oversight from your engineering manager.
- Identify and resolve ambiguity in requirements, system boundaries, and design tradeoffs without waiting for a fully-formed spec.
- Mentor L3/L4 engineers on the team: code reviews, design feedback, pairing, and raising the bar for what production-quality work looks like.
- Partner with your L6 technical lead and the Distinguished Engineer on architectural decisions, surfacing tradeoffs clearly rather than deferring them upward.
- Contribute to cross-team engineering standards: you are expected to influence practices beyond your immediate squad.
- Translate technical context into clear written artifacts that non-engineers (PM, Support, Leadership) can act on.
- Participate in on-call rotation and own the full incident lifecycle for your system: detection, diagnosis, resolution, and retrospective.
What You'll Do
- Define the evaluation methodology for content classification at Securly: establish what a model card must contain and hold every model release to that standard before it ships.
- Lead the multiclass refactor of Securly's content classification models: redesign binary models to handle multi-label, multi-class content categories (Adult Content, Violence, Self-Harm, Social Media, and others).
- Build and maintain labeled evaluation datasets with robust annotation workflows; address class imbalance and label noise systematically; document dataset curation decisions in a versioned data card.
- Connect offline evaluation to production monitoring — surface classification drift and error patterns before they become customer-facing issues.
- Investigate and resolve misclassification errors: false positives (over-blocking) and false negatives (under-blocking); produce written root cause analyses.
- Build and maintain training data pipelines: ingestion, cleaning, labeling, and versioning at scale.
- Mentor the existing AI team on evaluation methodology, model development practices, and data science communication rigor.
- Communicate precision/recall tradeoffs to product managers and engineers; produce executive-level summaries of classification quality for leadership.
- Collaborate with engineering to integrate model outputs into the production filtering stack with appropriate latency and reliability constraints.
- Research and prototype improvements: feature representations, model architectures, active learning for label efficiency, domain adaptation for emerging content categories.
Skills & Requirements
Must-Have
- Machine learning — multi-label/multi-class classification, model evaluation methodology, handling class imbalance, feature engineering for text and URL data. 5+ years in applied ML roles.
- Python (ML stack) — production-quality code: scikit-learn, PyTorch or TensorFlow, pandas, numpy. Notebooks for exploration; production-grade pipelines for delivery.
- Text / NLP feature engineering — URL tokenization, domain analysis, HTML content features, TF-IDF or embedding-based representations for web content classification.
- ML evaluation rigor — precision/recall tradeoffs, confusion matrix analysis, offline vs. online evaluation, A/B testing, reproducible model cards. At L5, you define the evaluation standard.
- Data engineering for ML — training data pipelines, data versioning, handling noisy and partially labeled datasets, annotation workflow design.
- Technical communication and stakeholder influence — ability to present quantitative model quality findings to both engineering and non-technical leadership.
Strongly Preferred
- Large-scale classification in production — shipping models with latency and throughput constraints; understanding the gap between offline eval metrics and live production behavior.
- Active learning / annotation workflows — strategies for efficient label acquisition on large, imbalanced datasets.
- Cloud ML infrastructure — AWS SageMaker, GCP Vertex AI, or equivalent for training pipelines, experiment tracking, and model deployment.
Nice to Have
- Web content / URL classification domain — prior work on web categorization, safe browsing, or content policy systems.
- K-12 / CIPA compliance — understanding of regulated content categories and compliance requirements around false negative rates.
- LLM-based classification — zero-shot or few-shot content classification for emerging categories without labeled training data.
- Graph / network features — domain co-occurrence, DNS graph signals, or network-based features for domain classification at scale.
Who You Are
- You have shipped ML models to production and lived with the consequences — you know what model drift looks like and how to catch it before it becomes a customer issue.
- You treat evaluation as a first-class engineering artifact. A model without a model card is not finished — and you set and enforce that standard for the team.
- You define the methodology, not just apply it. You produce the evaluation framework that other data scientists use, and you hold them to it.
- You can communicate precision/recall tradeoffs to a product manager and to a senior engineer in the same conversation, calibrated to each audience.
- You are energized by problems with real stakes: a false negative in Self-Harm classification is not an acceptable error rate.
- You mentor by example and by expectation: your code, your analysis, and your documentation set the standard.
About Securly
Securly processes over 1.1 billion requests per day and 54 TB of data daily, protecting more than 20 million students across 20,000+ schools globally. Since pioneering the first cloud-based web filter for K-12 in 2013, Securly has built one of the most trusted, high-scale platforms for student safety, wellness, and engagement. By turning data into meaningful, actionable intelligence, Securly enables schools to identify risk earlier, reduce harmful incidents, and strengthen student support.
We are proud to be consistently recognized as a Top Place to Work, named a Top 40 Most Used EdTech platform, and included on the GSV 150 list as one of the most transformational growth companies in digital learning and workforce skills.
Benefits
- Comprehensive Health Insurance (employee, parents, spouse, children)
- Accidental & Term Life Insurance
- Learning & Development reimbursement
- Paid Time Off
- Public Holidays (10+ per year)
- Retirement Benefits (EPF & gratuity)
- Parental Leave (as per statutory norms)
Securly is an Equal Opportunity Employer committed to inclusion, fairness, and respect. We welcome applicants from all backgrounds, identities, and experiences. #LI-REMOTE #LI-DO1
Ready to apply?
Apply to Securly
Staff Machine Learning Engineer
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About HG Insights
Headquartered in Santa Barbara, California, HG Insights is the global leader in technology intelligence. We help the world’s most innovative companies accelerate their go-to-market efforts with precision through advanced data science methodologies and proprietary data assets. We offer a culture that blends innovation, collaboration, and growth, where each team member is empowered to make a measurable impact.
Role Overview
We are looking for a Staff/Senior Machine Learning Engineer to join our growing AI and Data Platform team in Pune, India. In this role, you will be responsible for designing, building, and scaling ML systems that power our core data intelligence products. You’ll work at the intersection of data engineering and machine learning, collaborating closely with data scientists, software engineers, and product teams to turn models and agents into robust, production-ready systems.
This is a high-impact role suited for someone who thrives on ownership, scalability, and deploying real-world ML and AI solutions at scale.
Key Responsibilities
ML & AI System Development
- Design and build end-to-end ML pipelines, from data ingestion and feature engineering to model training, serving, and monitoring.
- Productionize AI agents, including LLM-backed workflows, multi-step tool use, and API orchestration.
- Collaborate with data scientists to bring experimental models into stable, high-performing production systems.
MLOps & Infrastructure
- Implement and maintain MLOps workflows (CI/CD for ML), tracking experiments, managing model versions, and automating retraining.
- Set up observability for models and agents, including tracing, logging, latency/error tracking, and fallback logic.
- Ensure infrastructure supports scalability, performance, and compliance requirements.
Technical Leadership & Collaboration
- Lead technical architecture for key AI and ML systems, ensuring maintainability and extensibility.
- Partner with product and platform teams to translate business needs into reliable and performant ML solutions.
- Mentor junior engineers and help elevate technical practices across the engineering org.
Minimum Qualifications
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
- 8+ years of industry experience, with at least 4+ years in ML engineering or MLOps.
- Strong programming skills in Python and experience with ML libraries such as PyTorch, TensorFlow, and scikit-learn.
- Experience deploying AI agents in production, including retrieval-augmented generation (RAG), multi-step reasoning, and tool-use orchestration.
- Familiarity with LangChain, LlamaIndex, Haystack, or similar frameworks for agent development.
- Hands-on with LLMs (OpenAI, Anthropic, Cohere, etc.), embedding models, and vector store integration.
- Expertise in MLOps tools like MLflow, Airflow, SageMaker, or Kubeflow.
- Experience with cloud platforms (preferably AWS), Docker/Kubernetes, and large-scale data systems (e.g., Snowflake, Databricks).
- Excellent problem-solving and system design skills, including CI/CD, testing, and infrastructure-as-code.
Preferred Qualifications
- Experience with streaming pipelines, online inference, and model monitoring at scale.
- Exposure to agent memory, feedback loops, or AI workflow orchestration in customer-facing products.
- Prior contributions to open-source ML or LLM projects are a plus.
Why Join HG Insights?
- Work on high-impact AI products used by global enterprise customers.
- Lead the ML engineering strategy and shape our AI platform.
- Competitive compensation and benefits tailored for India-based employees.
- A collaborative and innovation-focused team environment.
Ready to build the future of AI infrastructure? Apply now to join HG Insights as a Staff Machine Learning Engineer in Pune.
Ready to apply?
Apply to HG Insights
About Us
“Capco, a Wipro company, is a global technology and management consulting firm. Awarded with Consultancy of the year in the British Bank Award and has been ranked Top 100 Best Companies for Women in India 2022 by Avtar & Seramount. With our presence across 32 cities across globe, we support 100+ clients across banking, financial and Energy sectors. We are recognized for our deep transformation execution and delivery.
WHY JOIN CAPCO?
You will work on engaging projects with the largest international and local banks, insurance companies, payment service providers and other key players in the industry. The projects that will transform the financial services industry.
MAKE AN IMPACT
Innovative thinking, delivery excellence and thought leadership to help our clients transform their business. Together with our clients and industry partners, we deliver disruptive work that is changing energy and financial services.
#BEYOURSELFATWORK
Capco has a tolerant, open culture that values diversity, inclusivity, and creativity.
CAREER ADVANCEMENT
With no forced hierarchy at Capco, everyone has the opportunity to grow as we grow, taking their career into their own hands.
DIVERSITY & INCLUSION
We believe that diversity of people and perspective gives us a competitive advantage.
MAKE AN IMPACT
Job Details
Lead AI Engineer
Location: Pune (Hybrid)
Role Purpose Design and deliver AI/ML and Generative AI solutions that enhance client intelligence, automation, and decision-making across Private Banking platforms, leveraging data sourced from core banking systems (e.g. Avaloq), CRM, and digital channels.
Key Responsibilities
- Develop and deploy AI/ML and GenAI models (e.g. NLP, summarization, recommendation engines)
- Build scalable data pipelines and feature engineering workflows using structured and unstructured data
- Integrate AI capabilities into enterprise platforms via APIs and microservices.
- Work with data originating from core banking platforms (e.g. Avaloq) including client, portfolio, and transaction data.
- Enable AI-driven use cases such as client insights, next-best-action, and automation of RM workflows.
- Implement model monitoring, governance, and performance optimization aligned to enterprise standards.
- Collaborate with engineering and business teams to translate wealth use cases into scalable solutions.
- Experience working with wealth management data (clients, portfolios, transactions)
- Understanding of data flows from core banking systems such as Avaloq
- Awareness of AI governance, model risk, and regulatory considerations in banking
Skills & Experience
Primary:
- Python (mandatory) • ML/DL frameworks (TensorFlow, PyTorch, Scikit-learn) • GenAI / LLM frameworks (LangChain, OpenAI, etc.) • AWS (S3, Lambda, SageMaker, Bedrock)
Secondary: • Data engineering (ETL pipelines, feature stores) • API development • Vector databases / embeddings
Ready to apply?
Apply to Capco