Job Description

Model Development & Optimization

Build, fine-tune, and optimize a variety of AI/ML models including supervised, unsupervised, reinforcement learning, and generative models. Design models for specific use cases such as Natural Language Understanding (NLU), Dialogue Management, Knowledge Retrieval, Named Entity Recognition (NER), Intent Classification, Recommendation Systems, and Question-Answering (QA). Implement advanced Gen AI models for dynamic content generation, chatbots, and contextual understanding.

AIOps and Model Lifecycle Management

Develop automated pipelines for model training, testing, and deployment. Monitor and manage the health of AI models in production using AIOps techniques. Ensure continuous improvement and retraining of models based on performance metrics and evolving data trends.

Data Engineering & Integration

Collaborate with Data Engineers to build data pipelines, perform ETL (Extract, Transform, Load), and preprocess large datasets. Implement data validation and entity resolution models for accurate information retrieval. Integrate AI models with external systems like SAP, ServiceNow, and other business-critical applications.

Cross-Functional Collaboration

Partner with UI/UX Designers to integrate AI solutions into user-facing products. Work with Full Stack Developers to ensure seamless integration of AI models into both backend and frontend systems. Engage with QA Engineers to validate model robustness and accuracy through rigorous testing protocols.

AI Governance & Ethical AI

Develop and enforce guidelines to ensure models are ethical, transparent, and free from biases. Implement data governance, model documentation, and compliance checks as part of the AI development lifecycle. Conduct periodic reviews to ensure alignment with responsible AI practices.

AI Research & Innovation

Stay up-to-date with the latest advancements in AI/ML, including new generative AI technologies. Experiment with emerging models and frameworks to continually push the boundaries of AI solutions within the organization. Drive thought leadership through internal knowledge sharing, AI workshops, and external publications.
Required Skills


5+ years of experience in AI/ML engineering, data science, or a related field. Proven expertise in building models using frameworks such as

TensorFlow, PyTorch, and scikit-learn

. Proficiency in

Python, SQL

, and experience with

Azure

(preferred),

AWS

, or

Google Cloud

for scalable AI/ML solutions. Strong understanding of

Natural Language Processing (NLP), Computer Vision, Generative AI

, and other advanced ML techniques. Experience with AI-driven solutions for dialogue management, NER, NLU, QA, OCR, and knowledge retrieval. Practical knowledge in integrating AI models with

SAP

,

ServiceNow

, or similar enterprise systems. Hands-on experience in using experiment tracking tools like

Weights & Biases (W&B)

, and proficiency with AIOps tools and techniques.

Preferred Skills

Familiarity with

Generative AI models

such as

GPT-3, DALL-E, BERT

, etc., and their practical applications. Experience with

AIOps

practices for automating model lifecycle management. Knowledge of

responsible AI, ethics

, and bias mitigation in production environments. * Advanced certification in AI/ML or cloud platforms like

Azure, AWS, or Google Cloud

(e.g., Microsoft Certified: Azure AI Engineer, AWS Certified Machine Learning, or Google Professional Machine Learning Engineer).