Junior GenAI Engineer

Company Description

Bosch Global Software Technologies Private Limited is a 100% owned subsidiary of Robert Bosch GmbH, one of the world's leading global supplier of technology and services, offering end-to-end Engineering, IT and Business Solutions. With over 28,200+ associates, it's the largest software development center of Bosch, outside Germany, indicating that it is the Technology Powerhouse of Bosch in India with a global footprint and presence in the US, Europe and the Asia Pacific region.

Job Description

Job Summary :

We are seeking a talented and motivated Junior GenAI Engineer with 3-4 years of professional experience to join our innovative and growing team. In this role, you will play a crucial part in designing, developing, and deploying advanced Generative AI models, with a particular focus on Text Analytics, Natural Language Processing (NLP), and Deep Learning techniques. You will work on challenging problems that directly impact our products and services, contributing to the next generation of intelligent systems.

Roles & Responsibilities :

• Model Development & Implementation: Design, develop, and implement Generative AI models (e.g., LLMs, GANs, VAEs for text) for various applications, leveraging your expertise in Text Analytics, NLP, and Deep Learning.

• Data Preprocessing & Feature Engineering: Clean, preprocess, and analyze large textual datasets to prepare them for model training. Design and implement effective feature engineering strategies for NLP tasks.

• Experimentation & Optimization: Conduct rigorous experimentation to evaluate model performance, identify areas for improvement, and optimize model architectures and hyperparameters.
• Research & Innovation: Stay abreast of the latest research and advancements in Generative AI, NLP, and Deep Learning. Propose and explore novel approaches to solve complex problems.
• Deployment & MLOps (early exposure): Assist in the deployment of AI models into production environments, contributing to monitoring, maintenance, and performance tuning. (Opportunity to learn and grow into MLOps practices).
• Collaboration & Communication: Work closely with senior engineers, data scientists, product managers, and other stakeholders to understand requirements, share insights, and deliver impactful solutions.
• Documentation: Document code, models, and processes thoroughly to ensure maintainability and knowledge sharing.

Qualifications

Educational qualification:

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related quantitative field.

Experience :

• 3-4 years of professional experience in roles focused on Machine Learning, Deep Learning, Natural Language Processing, or Text Analytics

Mandatory/requires Skills :

Strong Foundation in NLP & Text Analytics -

• Solid understanding of core NLP concepts (e.g., tokenization, stemming, lemmatization, parsing, named entity recognition, sentiment analysis).
• Experience with various NLP techniques and libraries (e.g., NLTK, SpaCy, Transformers).
• Proven ability to work with large unstructured text datasets.

Deep Learning Expertise:

• Hands-on experience with deep learning frameworks such as TensorFlow, PyTorch, or JAX.
• Proficiency in implementing various neural network architectures (e.g., CNNs, RNNs, LSTMs, Transformers).
• Understanding of key deep learning concepts like backpropagation, optimization algorithms, and regularization.

Generative AI Interest/Exposure:

• Strong interest in and foundational understanding of Generative AI concepts (e.g., LLMs, diffusion models, GANs, VAEs, attention mechanisms).
• Experience working with pre-trained language models (e.g., BERT, GPT, T5) for fine-tuning and adaptation.

Programming Proficiency:

• Expert-level proficiency in Python, including relevant libraries for data science and machine learning (NumPy, Pandas, Scikit-learn).

Problem-Solving Skills:

• Excellent analytical and problem-solving abilities, with a keen eye for detail.

Communication Skills:

• Strong verbal and written communication skills, with the ability to explain complex technical concepts clearly to both technical and non-technical audiences.

Team Player:

• Ability to work effectively in a collaborative team environment and contribute to a positive and innovative culture.

Good To Have :

• Experience with cloud platforms (AWS, Azure, GCP) for deploying and managing AI/ML workloads.

• Familiarity with MLOps principles and tools (e.g., MLflow, Kubeflow, Docker, Kubernetes).

• Experience with distributed computing frameworks (e.g., Spark, Dask).
• Contributions to open-source projects in NLP or Generative AI.
• Published research papers or participation in Kaggle competitions related to NLP/Deep Learning.
• Experience with prompt engineering and fine-tuning large language models for specific tasks.