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Job Description: AI EngineerRole Overview<ul><li>Client: Insight (Offshore)</li><li>Experience: 4 &ndash; 5+ Years</li><li>Location: Open for the right candidate (Remote / Flexible)</li><li>Compensation: Up to ₹28 LPA</li></ul>Position SummaryWe are seeking a highly skilled AI Engineer with 4 to 5+ years of experience to design, develop, and scale production-grade AI/LLM-based applications, multi-agent systems, and Advanced RAG pipelines. In this role, you will leverage cutting-edge agentic frameworks, advanced retrieval strategies, and AWS cloud architecture to transition complex AI prototypes into robust, secure, and highly scalable production systems.Key ResponsibilitiesAI &amp; Agentic Systems Development<ul><li>Design and implement complex AI agent architectures, autonomous multi-agent systems, and tool-calling workflows.</li><li>Build advanced RAG (Retrieval-Augmented Generation) pipelines utilizing blob storage, vector search, embeddings, reranking, and optimized document retrieval.</li><li>Create semantic search, hybrid search, and knowledge retrieval systems using BM25, cross-encoders, and bi-encoders.</li><li>Establish prompt engineering workflows, prompt management, and rigorous LLM evaluation strategies to mitigate hallucinations.</li><li>Implement streaming AI responses utilizing SSE (Server-Sent Events) or WebSockets.</li></ul>Backend &amp; Cloud Architecture<ul><li>Develop and maintain high-performance, asynchronous backend services and APIs using FastAPI.</li><li>Build, containerize, and deploy AI/LLM applications end-to-end on AWS ecosystems.</li><li>Seamlessly integrate core AWS services including Amazon Bedrock, S3, Lambda, ECS/EKS, OpenSearch, and various database systems.</li><li>Optimize AI workloads for low latency, high scalability, reliability, and cost-efficiency.</li></ul>Security, Observability, &amp; Collaboration<ul><li>Integrate AI evaluation, monitoring, and tracing frameworks to maintain system observability.</li><li>Enforce cloud security best practices, data governance, authentication, and guardrails for responsible AI.</li><li>Collaborate closely with DevOps and platform teams to build robust CI/CD pipelines for AI workloads.</li></ul>Pay: ₹511,174.60 - ₹2,500,000.00 per yearBenefits:<ul><li>Health insurance</li><li>Paid sick time</li><li>Provident Fund</li></ul>Work Location: In person

AI Engineer

Data Science Team (Senior &amp; Junior Roles) &ndash; Mulehunter.aiJob Location<ul><li>Mumbai, Maharashtra (On-site / Hybrid)</li></ul>About Mulehunter.ai &amp; Why These Roles MatterMulehunter.ai is an advanced AI/ML-based platform designed to detect mule accounts across the banking ecosystem, with a specialized focus on scaling fraud security within cooperative and rural banking networks. As new banks are onboarded, our platform must be continuously tuned to adapt to a new class of customers, products, and evolving transaction patterns.To support our rapid expansion, we are scaling our Mumbai-based Data Science team. We are looking for a Senior Data Scientist to provide technical leadership and architectural direction, alongside a Junior Data Scientist to drive hands-on execution, analysis, and experimentation.Role 1: Senior Data ScientistWhat You Will Do<ul><li>Technical Leadership: Lead end-to-end model design covering data exploration, feature engineering, training, calibration, validation, and production deployment.</li><li>Advanced Modeling: Train, evaluate, and scale supervised, unsupervised, and anomaly-detection models for identifying complex mule-account networks.</li><li>Feature Engineering: Partner with AML/FRM teams to translate banking insights and red-flag behaviors into high-performing, model-ready features.</li><li>Team &amp; Standards: Define ML standards, code-review practices, experiment tracking, and documentation norms. Mentor junior data scientists and review their technical execution.</li><li>Integration: Collaborate closely with data engineers and bank IT teams for seamless deployment via APIs.</li></ul>What We Are Looking For<ul><li>Education: Bachelor&rsquo;s degree in Computer Science, Data Science, AI/ML, or a related field (Master&rsquo;s preferred).</li><li>Experience: 6&ndash;8 years of hands-on experience in Data Science and MLOps, ideally within banking, financial crime, or AML domains.</li><li>Tech Stack: Strong programming in Python and SQL. Deep familiarity with ML frameworks (TensorFlow, PyTorch, scikit-learn, XGBoost, LightGBM) and large-scale transactional datasets.</li><li>Infrastructure: Comfort with on-prem ML environments (Anaconda/Jupyter, VS Code, Kubeflow, Git, PySpark).</li><li>Domain Knowledge: Strong understanding of AML concepts including RFIs, STR workflow, and alert-review processes.</li></ul>Role 2: Junior Data ScientistWhat You Will Do<ul><li>Data Prep &amp; EDA: Perform data quality checks, pre-processing, and exploratory data analysis to surface behavioral indicators and fraud red flags.</li><li>Model Support: Build, train, and evaluate baseline supervised and unsupervised models under the guidance of senior data scientists.</li><li>Production Monitoring: Monitor live model performance, analyze data drifts, investigate false positives/negatives, and propose tuning actions.</li><li>Feedback Loops: Work closely with the Enhanced Due Diligence (EDD) team to refine thresholds and continuously improve alert precision.</li><li>Documentation: Keep rigorous records of detection rules, model versions, and experiment results.</li></ul>What We Are Looking For<ul><li>Education: Bachelor&rsquo;s degree in Computer Science, Data Science, AI/ML, Statistics, or a related field.</li><li>Experience: 2&ndash;5 years of applied ML experience, preferably with exposure to banking, fraud, or transactional datasets.</li><li>Tech Stack: Proficiency in Python and SQL, with hands-on experience using pandas, NumPy, scikit-learn, XGBoost, and LightGBM. (PySpark is a major plus).</li><li>Core Concepts: Clear understanding of core classification, clustering, and anomaly-detection techniques.</li><li>Workflow: Comfort using Anaconda/Jupyter, VS Code, and Git-based version control, alongside an eagerness to learn financial compliance systems.</li></ul>What Success Looks Like for Our Team<ul><li>Production Excellence: Deploying highly accurate, production-grade fraud detection models across our participating banks.</li><li>Operational Efficiency: Materially reducing false-positive rates while significantly improving model interpretability for bank compliance officers.</li><li>Seamless Integration: Achieving successful validation and seamless integration with existing bank AML/FRM infrastructures.</li></ul>Pay: ₹600,242.14 - ₹1,976,587.39 per yearBenefits:<ul><li>Health insurance</li><li>Paid sick time</li><li>Provident Fund</li></ul>Work Location: In person

Data Scientist

Data Engineer &ndash; Mulehunter.aiJob Location<ul><li>Mumbai, Maharashtra (On-site / Hybrid)</li></ul>About Mulehunter.ai &amp; Why This Role MattersMulehunter.ai is an advanced AI/ML-based system designed to detect mule accounts across the banking ecosystem. Reliable, well-structured data pipelines are the critical foundation on which our modeling, alerting, and reporting workflows operate.In this role, you will design and execute robust data workflows to support advanced analytics and machine learning initiatives. Operating from our Mumbai office, you will collaborate closely with data scientists and business stakeholders to manage, transform, and operationalize large-scale financial and transactional datasets.What You Will Work On<ul><li>Data pipelines and infrastructure for Mulehunter.ai, managing large-scale banking datasets built to power AI/ML fraud detection at scale.</li></ul>What You Will Do<ul><li>Data Integration: Extract structured and unstructured data from diverse banking databases, ensuring secure, high-throughput, and efficient data flow into modeling pipelines.</li><li>Data Preprocessing: Clean raw financial datasets, handle missing values, normalize formats, and prepare complex account-level and transaction-level data for downstream ML applications.</li><li>BRE Query Development: Design and implement optimized SQL-based queries and logic for the Business Rule Engine (BRE) based on exploratory data analysis (EDA) insights.</li><li>ML Collaboration: Partner tightly with the Data Science team to support complex data preparation, automated feature engineering, and live ML experiments.</li><li>Pipeline Management: Build, maintain, schedule, and monitor production-grade ETL/ELT workflows to guarantee system reliability.</li><li>Security &amp; Compliance: Enforce data security protocol, strict access controls, and complete data auditability at every layer of the data pipeline.</li></ul>What We Are Looking For<ul><li>Experience: 3&ndash;8 years of core experience in data engineering, preferably within the BFSI, financial crime, or AML/fraud domains.</li><li>Core Technical Skills: Strong mastery of SQL, advanced data query optimization, and proficiency in Python for data manipulation (pandas, NumPy, etc.).</li><li>Database Systems: Hands-on experience with relational databases (PostgreSQL, MySQL, Oracle) and data warehousing solutions (Snowflake, Redshift, BigQuery, or Azure Synapse).</li><li>Big Data &amp; Orchestration: Solid experience with big data frameworks (Apache Spark, Hadoop) and pipeline automation/orchestration tools (Kafka, Apache Airflow).</li><li>Domain Understanding: Prior exposure to handling highly sensitive financial datasets, specifically account-level and transaction-level ledger data.</li></ul>What Success Looks Like<ul><li>Robust Infrastructure: Production-grade, highly reliable data pipelines consistently serving the machine learning modeling team.</li><li>Data Readiness: Clean, well-modeled, and structured datasets readily optimized for real-time analysis and model training.</li><li>Scalable Performance: Efficient, low-latency data infrastructure engineered to support high-volume fraud detection across the banking ecosystem.</li></ul>Pay: ₹510,440.24 - ₹1,868,453.81 per yearBenefits:<ul><li>Health insurance</li><li>Paid sick time</li><li>Provident Fund</li></ul>Work Location: In person

Data Engineer

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AI Engineer

Data Scientist

Data Engineer