Machine Learning Engineer

ENAM is looking for high-caliber individuals who combine strong machine learning and/or AI engineering expertise with the ability to work on real-world financial datasets to build usable systems. This is an opportunity to create a suite of greenfield solutions for one of India's most respected investment firms, which is on a journey to reimagine its operations. Selected individuals would learn immensely from real-life delivery experience within financial markets, working closely with domain experts and witnessing the business outcomes of solutions delivered. The role requires working full-time from ENAM's office in Mumbai. Remote/work-from-home options are unavailable currently.

Requirements:

• Candidates with primary strengths in one area (AI or ML), while knowledgeable of the other, are also eligible for further assessment.
• 4 - 10 years of experience in machine learning, data science, or AI engineering (strong AI engineers with < 4 years' experience can be considered).
• Strong hands-on programmer with a builder mindset, building solutions rather than completed tasks/components.
• Experience building and deploying models in production environments.

Technical Strengths:

• Proven experience training machine learning models on structured data.
• Strong understanding of model evaluation, overfitting, and statistical rigor.
• Experience with ML frameworks (e. g., PyTorch, TensorFlow, or similar).
• Ability to work with large datasets and build data pipelines.
• Familiarity with modern AI tools, LLMs, and their practical applications.

Data and Analytical Thinking:

• Strong problem-solving ability with a data-first mindset.
• Ability to extract insights from historical datasets (pattern recognition, anomaly detection, performance analysis).
• Comfortable working with messy, real-world data.

Engineering and Build Capability:

• Strong hands-on generative AI / LLM development experience.
• Experience building end-to-end applications using modern Agentic AI orchestration tools and frameworks.
• Solid Python engineering + microservices experience.
• Proven ability to design end-to-end GenAI architectures.
• Cloud-native deployment experience (Azure/AWS/GCP).
• Multimodal AI, knowledge graphs, and enterprise integrations.
• Domain Preference (important but not mandatory).
• Prior experience in finance, trading, fintech, or quantitative roles is highly preferred OR demonstrated interest in financial markets with the ability to quickly ramp up.