Job Description

As the Engineering Lead for Generative AI and Data Science, you will be responsible for leading the development and implementation of advanced AI solutions, particularly in the field of Generative AI, while overseeing the broader data science and engineering efforts. This role requires a deep understanding of AI/ML technologies, a strong technical background in data science, and proven leadership skills. You will work closely with cross-functional teams to deliver high-impact solutions that align with our strategic goals.

Key Attributes:

● Graduate Engineer or postgraduate with 07 – 09 years of experience, out of which about 1 to 1.5 years in advanced Generative AI technologies and the rest in Data Science, AI, and Machine Learning.

● Must have a good grasp of database management, including Vector DBs, SQL (Structured Query Language), and NoSQL (Non-relational) databases.

● Must have hands-on Python-based web and back-end frameworks like Django, flask

● Hands-on and end-to-end product build, development, and delivery experience with a mix of customer interfaces (eg web, mobile), languages and technologies, SaaS/Cloud based/ Big Data / Data Centered application, with a strong concurrent user base.

Must-Have Skills:

● Hands-on Gen AI experience across LLMs like OpenAI, Gemini, Mistral, Falcon, etc.

● Advanced RAG (Retrieval Augmented Generation), Multi-Modal RAG

● LangChain, LlAMA

● LLM Finetuning - PEFT, LoRA, QLoRA, Adapters etc.

● Text analytics: NLP, Sentiment Analysis, Topic Modelling, Seq2Seq, Attention model, Tokenizer, TFIDF, Word2Vec, etc

● Python-based web and back-end frameworks like Django, flask, fast API

● Tools: Python, Pytorch, Keras, Tensorflow, OpenCV, Spacy, Sklearn, Pandas, Git , SQL, Databricks, R, etc.

● Experience in Azure native Data Engineering, Data Analytics, AI/ML and GenAI capabilities

Nice-to-Have Skills:

● Machine learning techniques: Neural Networks, Deep Learning algorithms (CNN/RNN/LSTM, etc), Predictive modeling, Exploratory Data analysis (EDA), Linear Regression, Logistic Regression, Clustering Techniques, Naïve Bayes, Ensemble, Gradient Boosting, XGBoost, Support Vector Machine (SVM), Decision Tree, Random forest, Knowledge Graphs etc.

Soft Skills:

● Excellent leadership and team management skills, with a track record of successfully leading crossfunctional teams. ● Strong problem-solving abilities and a proactive approach to identifying and addressing challenges.

● Excellent communication skills, with the ability to articulate complex technical concepts to both technical and non-technical stakeholders.

● Ability to work in a fast-paced, dynamic environment with a strong focus on delivering high-quality results.

Key Responsibilities:

● Cross-functional collaboration: Work closely with product management, engineering, and business teams to integrate AI-driven features into products and services, ensuring they meet market needs and customer expectations. ● Data Pipeline Management: Oversee the creation and maintenance of scalable data pipelines, ensuring efficient data collection, processing, and storage.

● Investigate the technology trends and implement them within the product following the product vision, strategy, and roadmap. ● Carry out quick POCs/ build MVPs to rapidly demonstrate solution possibilities/feasibilities across Infrastructure, Storage, Analytics, AI/ML, Security etc,

● Ensure efficient usage of tools and implementation of processes and systems thus creating a proficient environment for effective delivery.

● Predict technology’s application for business. This may include long-term tech trends or the impact of the technology element on a company’s roadmap.

● Assess the timeframes for the development team and be responsible for every release the company does. Responsible for product roadmaps.

● Work with clients to ensure smooth and successful implementation, delivery, and deployment of complex solutions

● Compliance: Ensure all AI and data science activities comply with relevant data privacy laws, regulations, and ethical standards.

● Client Interaction: Engage with clients to understand their needs and ensure successful implementation, delivery, and deployment of AI-driven solutions.

● Business Development Support: Collaborate with the business development team to support solutioning, estimations, and participation in RFI/RFP processes