Employment Type : Full-Time.Relevant Experience : 10+ years
Key Responsibilities
- AI-First Leadership : Define and drive DB Techs AI vision, re-architect systems into AI-native services,
integrate tools like Cursor/Relevance AI, and mentor teams in prompt engineering, Vibe Coding, and
autonomous testing.
- Architect Scalable AI Systems : Design enterprise-scale AI/ML platforms that support real-time analytics,
model deployment, and continuous learning in financial products and services.
- Lead Solution Design : Collaborate with data scientists, engineers, and business stakeholders to build and
integrate AI models into core platforms (e.g., risk engines, transaction monitoring, robo-advisors).
- Ensure Governance & Compliance : Implement AI systems that meet financial regulations (e.g., GDPR,
PCI-DSS, FFIEC, Basel III) and uphold fairness, explainability, and accountability.
- Drive MLOps Strategy : Establish and maintain robust pipelines for data ingestion, feature engineering,
model training, testing, deployment, and monitoring.
- Team Leadership : Provide technical leadership to data science and engineering teams. Promote best
practices in AI ethics, version control, and reproducibility.
- Identify areas where AI can deliver business value and lead the development of proofs-of-concept
(PoCs).
- Evaluate the feasibility, cost, and impact of new AI initiatives.
- Define best practices\standards for model lifecycle management (training, validation, deployment,
monitoring)- Evaluate Emerging Technologies : Stay ahead of developments in generative AI, LLMs, and FinTech-specific AI tools, and drive their strategic adoption.
Technical Skills And Tools
ML & AI Frameworks :
- Scikit-learn, XGBoost, LightGBM, TensorFlow, PyTorch
- Hugging Face Transformers, OpenAI APIs (for generative and NLP use cases)
MLOps & Deployment
- MLflow, Kubeflow, Seldon Core, KServe, Weights & Biases
- FastAPI, gRPC, Docker, Kubernetes, Airflow
FinTech-Specific Applications
- Credit scoring models, Fraud detection algorithms
- Time series forecasting, NLP for financial documents/chatbots
- Algorithmic trading models, AML (Anti-Money Laundering) systems
Cloud & Data Platforms
- AWS SageMaker, Azure ML, Google Vertex AI
- Databricks, Snowflake, Kafka, BigQuery, Delta Lake
Monitoring & Explainability
- SHAP, LIME, Alibi, Evidently AI, Arize AI
- IBM AIX 360, Fiddler AI
Required Qualifications
- Bachelors or Masters degree in Computer Science, Artificial Intelligence, Data Science, or a related field;
PhD is a plus.
- 10+ years of experience in AI/ML, including 35 years in architectural roles within FinTech or other highly
regulated industries.
- Proven track record of building and deploying AI solutions in areas such as fraud detection, credit risk
modeling, or portfolio optimization.
Strong Hands-on Expertise In
- Machine Learning : scikit-learn, XGBoost, LightGBM, TensorFlow, PyTorch
- Data Engineering : Spark, Kafka, Airflow, SQL/NoSQL (MongoDB, Neo4j)
- Cloud & MLOps : AWS, GCP, or Azure; Docker, Kubernetes, MLflow, SageMaker, Vertex AI
- Programming : Python (primary); Java or Scala (optional)
- Solid software engineering background with experience integrating ML models into scalable production
systems.
- Excellent communication skills with the ability to influence both technical and non-technical stakeholders.
(ref:hirist.tech)