AI ENGINEER (Experienced)

Brief DescriptionWe are looking for an experienced AI Engineer to join our team. The ideal candidate will have a strong background in designing, deploying, and maintaining advanced AI/ML models with expertise in Natural Language Processing (NLP), Computer Vision, and architectures like Transformers and Diffusion Models. You will play a key role in developing AI-powered solutions, optimizing performance, and deploying and managing models in production environments. Key Responsibilities

AI Model Development and Optimization:

Design, train, and fine-tune AI models for NLP, Computer Vision, and other domains using frameworks like TensorFlow and PyTorch.

Work on advanced architectures, including Transformer-based models (e.g., BERT, GPT, T5) for NLP tasks and CNN-based models (e.g., YOLO, VGG, ResNet) for Computer Vision applications.

Utilize techniques like PEFT (Parameter-Efficient Fine-Tuning) and SFT (Supervised Fine-Tuning) to optimize models for specific tasks.

Build and train RLHF (Reinforcement Learning with Human Feedback) and RL-based models to align AI behavior with real-world objectives.,

Explore multimodal AI solutions combining text, vision, and audio using generative deep learning architectures.

Natural Language Processing (NLP):

Develop and deploy NLP solutions, including language models, text generation, sentiment analysis, and text-to-speech systems.

Leverage advanced Transformer architectures (e.g., BERT, GPT, T5) for NLP tasks.

AI Model Deployment and Frameworks:

Deploy AI models using frameworks like VLLM, Docker, and MLFlow in production-grade environments.

Create robust data pipelines for training, testing, and inference workflows.

Implement CI/CD pipelines for seamless integration and deployment of AI solutions.

Production Environment Management:

Deploy, monitor, and manage AI models in production, ensuring performance, reliability, and scalability.

Set up monitoring systems using Prometheus to track metrics like latency, throughput, and model drift.

Data Engineering and Pipelines:

Design and implement efficient data pipelines for preprocessing, cleaning, and transformation of large datasets.

Integrate with cloud-based data storage and retrieval systems for seamless AI workflows.

Performance Monitoring and Optimization:

Optimize AI model performance through hyperparameter tuning and algorithmic improvements.

Monitor performance using tools like Prometheus, tracking key metrics (e.g., latency, accuracy, model drift, error rates etc.)

Solution Design and Architecture:

Collaborate with cross-functional teams to understand business requirements and translate them into scalable, efficient AI/ML solutions.

Design end-to-end AI systems, including data pipelines, model training workflows, and deployment architectures, ensuring alignment with business objectives and technical constraints.

Conduct feasibility studies and proof-of-concepts (PoCs) for emerging technologies to evaluate their applicability to specific use cases.

Stakeholder Engagement:

Act as the technical point of contact for AI/ML projects, managing expectations and aligning deliverables with timelines.

Participate in workshops, demos, and client discussions to showcase AI capabilities and align solutions with client needs. Experience: 2.5 - 5  years of experienceSalary : 5-11 LPAPreferred SkillsTechnical Skills

Proficient in Python, with strong knowledge of libraries like NumPy, Pandas, SciPy, and Matplotlib for data manipulation and visualization.

Expertise in TensorFlow, PyTorch, Scikit-learn, and Keras for building, training, and optimizing machine learning and deep learning models.

Hands-on experience with Transformer libraries like Hugging Face Transformers, OpenAI APIs, and LangChain for NLP tasks.

Practical knowledge of CNN architectures (e.g., YOLO, ResNet, VGG) and Vision Transformers (ViT) for Computer Vision applications.

Proficiency in developing and deploying Diffusion Models like Stable Diffusion, SDX, and other generative AI frameworks.

Experience with RLHF (Reinforcement Learning with Human Feedback) and reinforcement learning algorithms for optimizing AI behaviors.

Proficiency with Docker and Kubernetes for containerization and orchestration of AI workflows.

Hands-on experience with MLOps tools such as MLFlow for model tracking and CI/CD integration in AI pipelines.

Expertise in setting up monitoring tools like Prometheus and Grafana to track model performance, latency, throughput, and drift.

Knowledge of performance optimization techniques, such as quantization, pruning, and knowledge distillation, to improve model efficiency.

Experience in building data pipelines for preprocessing, cleaning, and transforming large datasets using tools like Apache Airflow, Luigi

Familiarity with cloud-based storage systems (e.g., AWS S3, Google BigQuery) for efficient data handling in AI workflows.

Strong understanding of cloud platforms (AWS, GCP, Azure) for deploying and scaling AI solutions.

Knowledge of advanced search technologies such as Elasticsearch for indexing and querying large datasets.

Familiarity with edge deployment frameworks and optimization for resource-constrained environments.