{ "generated_at": "2026-09-26T18:01:11+00:00", "note": "Salaries are indicative, compiled from published guides. Postings are curated by CapabilityNext staff.", "roles": [ { "slug": "ml-engineer", "title": "AI / Machine Learning Engineer", "summary": "Builds, trains, evaluates and ships models. The broad, portable role most people target first.", "salary_map_url": "https://capabilitynext.com/salary-map#ml-engineer", "path_note": "Path: ~25 weeks + the 2-week foundations sprint ≈ 6–7 months part-time · total ₹1,06,000 + GST · target entry ₹6–12 LPA.", "skills": [ { "name": "Supervised & unsupervised learning, evaluation", "label": "Supervised & unsupervised learning, evaluation", "importance": "core", "course": "intro-to-ml" }, { "name": "Deep learning with PyTorch", "label": "Deep learning with PyTorch", "importance": "core", "course": "deep-learning-foundations" }, { "name": "Turning a model into a service", "label": "Turning a model into a service", "importance": "core", "course": "mlops-on-the-cloud" }, { "name": "Python, NumPy / pandas, the ML maths", "label": "Python, NumPy / pandas, the ML maths", "importance": "core", "course": "python-maths-for-ml" }, { "name": "Linear algebra & calculus for ML", "label": "Linear algebra & calculus for ML", "importance": "important", "course": "python-maths-for-ml" }, { "name": "Probability & statistics for ML", "label": "Probability & statistics for ML", "importance": "important", "course": "python-maths-for-ml" }, { "name": "Supervised & unsupervised ML, evaluation", "label": "Supervised & unsupervised ML, evaluation", "importance": "important", "course": "intro-to-ml" }, { "name": "Bias, leakage & explainability", "label": "Bias, leakage & explainability", "importance": "important", "course": "intro-to-ml" } ], "path": [ { "course": "python-maths-for-ml", "either_or": null }, { "course": "intro-to-ml", "either_or": null }, { "course": "deep-learning-foundations", "either_or": null }, { "course": "mlops-on-the-cloud", "either_or": null } ], "bands": [ { "band": "entry", "low": 6, "median": 9, "high": 12, "unit": "LPA", "currency": "INR", "note": "India, entry (with portfolio)", "min_years": null, "max_years": null, "indicative": true, "sources": [] }, { "band": "mid", "low": 15, "median": 22.5, "high": 30, "unit": "LPA", "currency": "INR", "note": "India, 2–5 years", "min_years": null, "max_years": null, "indicative": true, "sources": [] } ], "postings": [] }, { "slug": "cv-nlp-engineer", "title": "Computer Vision / NLP Engineer", "summary": "A deep-learning specialisation — image models or language models — on top of the ML-engineer base.", "salary_map_url": "https://capabilitynext.com/salary-map#cv-nlp-engineer", "path_note": "Path: ~26–32 weeks ≈ 6–8 months part-time (Python & Maths first if needed) · total ₹1,24,500 + GST · target entry ₹7–13 LPA.", "skills": [ { "name": "Deep learning, CNNs and transformers", "label": "Deep learning, CNNs and transformers", "importance": "core", "course": "deep-learning-foundations" }, { "name": "Detection / segmentation, or sequence tasks", "label": "Detection / segmentation, or sequence tasks", "importance": "core", "course": "applied-computer-vision" }, { "name": "Data pipelines, augmentation, transfer learning", "label": "Data pipelines, augmentation, transfer learning", "importance": "core", "course": "applied-computer-vision" }, { "name": "Evaluation and deployment of the model", "label": "Evaluation and deployment of the model", "importance": "core", "course": "nlp-with-transformers" }, { "name": "Vision models — detection, segmentation", "label": "Vision models — detection, segmentation", "importance": "important", "course": "applied-computer-vision" }, { "name": "Language models — transformers, NER, seq2seq", "label": "Language models — transformers, NER, seq2seq", "importance": "important", "course": "nlp-with-transformers" }, { "name": "Multimodal & vision-language systems", "label": "Multimodal & vision-language systems", "importance": "nice", "course": "multimodal-systems" }, { "name": "Tokenisation & text embeddings", "label": "Tokenisation & text embeddings", "importance": "important", "course": "nlp-with-transformers" }, { "name": "Image–text embeddings & retrieval", "label": "Image–text embeddings & retrieval", "importance": "nice", "course": "multimodal-systems" }, { "name": "Document understanding & OCR pipelines", "label": "Document understanding & OCR pipelines", "importance": "nice", "course": "multimodal-systems" } ], "path": [ { "course": "intro-to-ml", "either_or": null }, { "course": "deep-learning-foundations", "either_or": null }, { "course": "applied-computer-vision", "either_or": null }, { "course": "nlp-with-transformers", "either_or": null }, { "course": "multimodal-systems", "either_or": null } ], "bands": [ { "band": "entry", "low": 7, "median": 10, "high": 13, "unit": "LPA", "currency": "INR", "note": "India, entry", "min_years": null, "max_years": null, "indicative": true, "sources": [] }, { "band": "mid", "low": 16, "median": 23, "high": 30, "unit": "LPA", "currency": "INR", "note": "India, 2–5 years", "min_years": null, "max_years": null, "indicative": true, "sources": [] } ], "postings": [] }, { "slug": "data-engineer", "title": "Data Engineer", "summary": "Builds the pipelines and stores that everything else depends on. The lowest-friction entry into the field.", "salary_map_url": "https://capabilitynext.com/salary-map#data-engineer", "path_note": "Path: ~17 weeks (26 with MLOps) ≈ 4–6 months part-time · total ₹81,000 + GST · target entry ₹5–10 LPA.", "skills": [ { "name": "SQL & Python, data modelling", "label": "SQL & Python, data modelling", "importance": "core", "course": "data-engineering-for-ml" }, { "name": "Batch & streaming pipelines, orchestration", "label": "Batch & streaming pipelines, orchestration", "importance": "core", "course": "data-engineering-for-ml" }, { "name": "Data quality, testing, cloud warehouses", "label": "Data quality, testing, cloud warehouses", "importance": "core", "course": "data-engineering-for-ml" }, { "name": "Feature stores for ML", "label": "Feature stores for ML", "importance": "core", "course": "data-engineering-for-ml" }, { "name": "Pipelines, orchestration & data quality", "label": "Pipelines, orchestration & data quality", "importance": "important", "course": "data-engineering-for-ml" }, { "name": "Python, NumPy / pandas, the ML maths", "label": "Python, NumPy / pandas, the ML maths", "importance": "nice", "course": "python-maths-for-ml" } ], "path": [ { "course": "python-maths-for-ml", "either_or": null }, { "course": "data-engineering-for-ml", "either_or": null }, { "course": "mlops-on-the-cloud", "either_or": null } ], "bands": [ { "band": "entry", "low": 5, "median": 7.5, "high": 10, "unit": "LPA", "currency": "INR", "note": "India, entry", "min_years": null, "max_years": null, "indicative": true, "sources": [] }, { "band": "mid", "low": 10, "median": 15, "high": 20, "unit": "LPA", "currency": "INR", "note": "India, 2–5 years", "min_years": null, "max_years": null, "indicative": true, "sources": [] } ], "postings": [] }, { "slug": "devops-to-mlops", "title": "DevOps Engineer → Platform / MLOps", "summary": "If you already do DevOps, you keep most of your toolkit and add the ML-specific parts. The fastest cross-over path.", "salary_map_url": "https://capabilitynext.com/salary-map#devops-to-mlops", "path_note": "Path: ~18 weeks ≈ 4–5 months part-time · total ₹53,000 + GST · MLOps roles typically pay ₹3–6 LPA more than the equivalent DevOps role.", "skills": [ { "name": "CI/CD, containers, Kubernetes, IaC", "label": "CI/CD, containers, Kubernetes, IaC", "importance": "core", "course": "mlops-on-the-cloud" }, { "name": "Cloud, observability, on-call", "label": "Cloud, observability, on-call", "importance": "core", "course": "mlops-on-the-cloud" }, { "name": "ML pipelines, feature stores, model registries", "label": "ML pipelines, feature stores, model registries", "importance": "core", "course": "mlops-on-the-cloud" }, { "name": "Model serving, batching, drift & retraining", "label": "Model serving, batching, drift & retraining", "importance": "core", "course": "llmops-and-serving" }, { "name": "Cloud deployment & ML-platform basics", "label": "Cloud deployment & ML-platform basics", "importance": "important", "course": "mlops-on-the-cloud" }, { "name": "Deploy, monitor & retrain models (CI/CD)", "label": "Deploy, monitor & retrain models (CI/CD)", "importance": "nice", "course": "mlops-on-the-cloud" } ], "path": [ { "course": "intro-to-ml", "either_or": null }, { "course": "mlops-on-the-cloud", "either_or": null } ], "bands": [ { "band": "entry", "low": 5, "median": 7, "high": 9, "unit": "LPA", "currency": "INR", "note": "India DevOps, entry", "min_years": null, "max_years": null, "indicative": true, "sources": [] }, { "band": "mid", "low": 12, "median": 17, "high": 22, "unit": "LPA", "currency": "INR", "note": "India MLOps, mid", "min_years": null, "max_years": null, "indicative": true, "sources": [] } ], "postings": [] }, { "slug": "llm-engineer", "title": "LLM / Generative-AI Engineer", "summary": "Designs, builds and ships LLM products — RAG, agents, evaluation, guardrails. Currently the highest-paid entry point.", "salary_map_url": "https://capabilitynext.com/salary-map#llm-engineer", "path_note": "Path: ~23 weeks (or ~31 with foundations) ≈ 5.5–7.5 months part-time · total ₹1,15,000 + GST · target entry ₹8–14 LPA.", "skills": [ { "name": "Prompt design & structured output", "label": "Prompt design & structured output", "importance": "core", "course": "llm-application-engineering" }, { "name": "Retrieval-augmented generation, vector search", "label": "Retrieval-augmented generation, vector search", "importance": "core", "course": "rag-and-vector-search" }, { "name": "Tool use, agents, evaluation suites", "label": "Tool use, agents, evaluation suites", "importance": "core", "course": "fine-tuning-and-evaluation" }, { "name": "Fine-tuning (LoRA), guardrails, cost / latency", "label": "Fine-tuning (LoRA), guardrails, cost / latency", "importance": "core", "course": "fine-tuning-and-evaluation" }, { "name": "Tool use & agentic workflows", "label": "Tool use & agentic workflows", "importance": "core", "course": "llm-application-engineering" }, { "name": "Prompting, RAG, agents & guardrails", "label": "Prompting, RAG, agents & guardrails", "importance": "important", "course": "llm-application-engineering" }, { "name": "Retrieval & vector search in production", "label": "Retrieval & vector search in production", "importance": "important", "course": "rag-and-vector-search" }, { "name": "Fine-tuning (LoRA) & model evaluation", "label": "Fine-tuning (LoRA) & model evaluation", "importance": "important", "course": "fine-tuning-and-evaluation" }, { "name": "Retrieval evaluation & grounded citations", "label": "Retrieval evaluation & grounded citations", "importance": "important", "course": "rag-and-vector-search" }, { "name": "LLM serving & inference at scale", "label": "LLM serving & inference at scale", "importance": "nice", "course": "llmops-and-serving" }, { "name": "Quantisation, distillation & inference optimisation", "label": "Quantisation, distillation & inference optimisation", "importance": "nice", "course": "llmops-and-serving" } ], "path": [ { "course": "python-maths-for-ml", "either_or": null }, { "course": "llm-application-engineering", "either_or": null }, { "course": "rag-and-vector-search", "either_or": null }, { "course": "fine-tuning-and-evaluation", "either_or": null }, { "course": "llmops-and-serving", "either_or": null } ], "bands": [ { "band": "entry", "low": 8, "median": 11, "high": 14, "unit": "LPA", "currency": "INR", "note": "India, entry", "min_years": null, "max_years": null, "indicative": true, "sources": [] }, { "band": "mid", "low": 18, "median": 26.5, "high": 35, "unit": "LPA", "currency": "INR", "note": "India, 2–5 years", "min_years": null, "max_years": null, "indicative": true, "sources": [] } ], "postings": [] }, { "slug": "mlops-engineer", "title": "MLOps Engineer", "summary": "Packages, deploys, monitors and retrains models. Where software / DevOps engineers most naturally cross into AI.", "salary_map_url": "https://capabilitynext.com/salary-map#mlops-engineer", "path_note": "Path: ~18 weeks (add ~6 for LLMOps) ≈ 5–7 months part-time · total ₹99,500 + GST · target entry ₹6–10 LPA.", "skills": [ { "name": "CI/CD for ML, model registries", "label": "CI/CD for ML, model registries", "importance": "core", "course": "mlops-on-the-cloud" }, { "name": "Pipelines & orchestration, feature stores", "label": "Pipelines & orchestration, feature stores", "importance": "core", "course": "data-engineering-for-ml" }, { "name": "Containerised serving, autoscaling", "label": "Containerised serving, autoscaling", "importance": "core", "course": "mlops-on-the-cloud" }, { "name": "Monitoring, drift detection, retraining", "label": "Monitoring, drift detection, retraining", "importance": "core", "course": "mlops-on-the-cloud" }, { "name": "Deploy, monitor & retrain models (CI/CD)", "label": "Deploy, monitor & retrain models (CI/CD)", "importance": "important", "course": "mlops-on-the-cloud" }, { "name": "Cloud deployment & ML-platform basics", "label": "Cloud deployment & ML-platform basics", "importance": "important", "course": "mlops-on-the-cloud" }, { "name": "Model serving, monitoring & retraining", "label": "Model serving, monitoring & retraining", "importance": "important", "course": "mlops-on-the-cloud" }, { "name": "LLM serving & inference at scale", "label": "LLM serving & inference at scale", "importance": "nice", "course": "llmops-and-serving" }, { "name": "Quantisation, distillation & inference optimisation", "label": "Quantisation, distillation & inference optimisation", "importance": "nice", "course": "llmops-and-serving" } ], "path": [ { "course": "intro-to-ml", "either_or": null }, { "course": "data-engineering-for-ml", "either_or": null }, { "course": "mlops-on-the-cloud", "either_or": null }, { "course": "llmops-and-serving", "either_or": null } ], "bands": [ { "band": "entry", "low": 6, "median": 8, "high": 10, "unit": "LPA", "currency": "INR", "note": "India, entry", "min_years": null, "max_years": null, "indicative": true, "sources": [] }, { "band": "mid", "low": 12, "median": 17, "high": 22, "unit": "LPA", "currency": "INR", "note": "India, 2–5 years", "min_years": null, "max_years": null, "indicative": true, "sources": [] } ], "postings": [] } ] }