Senior AI Engineer

Main responsibilities

• As a Senior AI Engineer, you’ll be part of growing engineering team and help to build the next generation AI
Solutions.

• Collaborate with business stakeholders to understand use cases and define AI solution; work on Proof of
Concepts wherever needed

• Engineer and deploy ML models into production using MLOps best practices (model versioning, monitoring,
CI/CD, etc.).

• Build & maintain data pipelines and model performance for scalability and maintainability.

• Ensure all models adhere to organizational AI policies, responsible AI practices, and audit requirements.

• Support data exploration, feature engineering, and occasional model building where needed.

• Automate model retraining, testing, and monitoring to ensure performance over time.

• Document ML workflows, governance checkpoints, and risk assessments.

• Partner with CloudOps,DevOps, IT, and security teams to integrate solutions into enterprise platforms.

The position requires autonomy and reliability in performing duties while maintaining close communication with rest of stake-holders.

Qualifications and Profile

Mandatory:

• Have degree or master’s degree in the field of AI / ML and data science with proven ability to design and
develop models

• 8+ years of experience in software development, data science and ML, with at least 3+ years in AI engineering
roles.

• Proven experience in end-to-end ML lifecycle: data wrangling, model development, deployment, and
monitoring.

• Strong programming skills in Python with Solid knowledge of AI/ML, including LLMs and data science libraries
like pandas, scikit-learn, TensorFlow/PyTorch, etc.

• Experience with LLM Orchestration frameworks like Langchain, LangGraph, vLLM, LMDeploy.

• Strong knowledge in NoSQL databases (any experience in Graph database is desirable)

• Experience with MLOps tools: MLflow, Airflow, Kubeflow, or similar.

• Familiarity with either of cloud platforms (GCP, AWS) for AI Solutioning and ML deployment.

• Knowledge of data science techniques including supervised/unsupervised learning, NLP, time series, etc.

• Experience with CI/CD pipelines and containerization (Docker, Kubernetes).

• Strong understanding of AI governance, model risk management, and regulatory requirements in AI.

• Ability to communicate technical concepts to non-technical stakeholders.

Preferred skills:

• Experience with Responsible AI frameworks and bias/fairness testing.

• Exposure to feature stores, model registries, and data versioning.

• Knowledge of data privacy, anonymization, and compliance in regulated industries (e.g., banking, healthcare).

Other Professional Skills and Mind-set

• Ability and willingness to learn and adopt new technologies

• Strong organizational and communication skills

• Strong analytical and problem solving skills

• Awareness of various software development procedures

• Ability to follow defined procedures

• Understanding and respect of cultural diversity