Principal AI Architect - MLOps/NLP

Job Description : Principal AI Architect

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.