Quick take: SEBI's Machine Learning Officer (Market Surveillance) is a 2-year renewable contract role, ₹1.3–1.8L/month, requiring 4+ years ML experience in fraud/anomaly detection. B.Tech/M.Tech mandatory. Direct application via online portal—no exam. GPS-eligible.
SEBI, India's financial regulator, is hiring Machine Learning Officers for its Market Surveillance Department in Mumbai on a 2-year renewable contract. This is a direct-application role — no entrance exam — where you'll work on fraud detection and market anomaly detection systems that protect India's securities markets. If you're a mid-level ML engineer with financial markets experience, this is a high-impact government role with ₹1,30,000–₹1,80,000/month compensation.
You need a B.Tech or M.Tech degree and at least 4+ years of experience applying machine learning to financial markets, fraud detection, or anomaly detection. This is a mid-level role suited for engineers who already know Python, XGBoost, and transformer-based models; who can write clean SQL; and who have hands-on experience with large-scale data processing. Regulatory awareness is a plus — if you've worked in fintech or compliance tech, even better. Freshers need not apply; SEBI is looking for practitioners ready to contribute immediately.
SEBI offers ₹1,30,000–₹1,80,000 per month for this contract role. For context, mid-level ML Engineer salaries in India typically range ₹80,000–₹1,60,000/month in private tech; SEBI's offer places you in the upper band, reflecting the seniority and regulatory criticality of the work. This is a government role with job security, fixed tenure, and potential renewal, so total compensation (salary + stability) compares well to private-sector AI engineer positions.
This is a direct-application role — no competitive exam required. Visit sebi.gov.in/recruitment and submit your application through the online portal. The deadline is June 4, 2026, so if you're ready, apply soon. You'll go through a technical assessment (likely coding + ML theory) and an interview before final selection.
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