Requirements:
- Bachelor's degree in any field with an emphasis on quantitative analysis and applied quantitative analysis such as Mathematics, Economics, Engineering, Statistics, Computer Science etc.
- At least 4 years of hands-on experience in artificial intelligence, machine learning,
software engineering, or a related technical field (e.g., full-stack or mobile engineering with AI systems). Experience should include designing, building, and deploying production-grade LLM/AI solutions, developing agentic workflows, and operationalizing AI systems with strong engineering practices (e.g., orchestration frameworks, evaluation pipelines, security guardrails, and performance optimization).
- Prior experience deploying and maintaining AI solutions in production environments, including monitoring, iteration, and optimization, is required.
- Industry preference: Banking and Financial Service Industry (BFSI), Pharma and health care, entertainment, ecommerce, retail and wholesale and manufacturing.
- Personal Projects (good to have): Experience building side projects in ML/AI/GenAI, including hands-on experimentation with emerging AI tools, frameworks, and platforms. This may include developing LLM-powered applications, agentic workflows, mobile or AI prototypes, evaluation frameworks, or lightweight production pipelines that demonstrate practical application, learning agility, and end-to-end ownership.
- Tech-stack: Next.Js, Typescript, Python, Postgres, DataBricks, Redis.
- AI & GenAI: NLP and multimodal systems; time-series analysis; Generative AI including RAG architectures, prompt engineering, tool use, and agentic patterns.
- Data & Storage: Strong proficiency in Python and SQL; experience with analytical processing (e.g., Spark); feature engineering; familiarity with vector databases (Postgres) and embedding pipelines.
- AI Engineering & Productionization: Production AI workflows including model and artifact versioning and experiment tracking.
- Cloud & Infrastructure: Hands-on experience with Cloud (Prefer: Azure stacks); performance optimization for inference and training workloads.
- Observability & Evaluation: End-to-end observability using logging, metrics, and tracing; model and agent monitoring; automated evaluation.
- Responsible AI & Security: Practical application of Responsible AI principles including privacy-by-design (PII handling, masking, de-identification), explainability, robustness, and alignment with regulatory and policy requirements.
- Coding: Apply strong engineering practices:
o Clean architecture and modular design
o Automated testing (unit, integration, E2E)
o Documentation and maintainability standards
- Preferred Traits: The company seeks a proactive, detail-oriented individual who is curious and passionate about data. They should be a team player with strong interpersonal skills.
- Analytical Thinking: Strong problem-solving skills and the ability to think critically about data.
- Attention to Detail: High level of accuracy and attention to detail in data analysis and reporting.
- Collaboration: Ability to work well with cross-functional teams and stakeholders.
- Adaptability: Flexibility to adapt to changing business needs and priorities.
- Thrill: Career Advancement Potential: This position offers the potential to move into senior
data Scientist roles, AI Engineering, ML System Architecting, MLOPs, or management roles within the data department, depending on performance and career aspirations.
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