AI ENGINEER (Experienced)

Overview

Required Skills

We 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

1. 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.

2. 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.

3. 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.

4. 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.

5. 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.

6. 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.)

7. 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.

8. 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.

Technical 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

Qualifications

· Bachelor's or Master's degree in Data Science, Statistics, Mathematics, Computer Science, or a related field.

Experience: 2.5 to 5 yrs

Location: Trivandrum

Job Type: Full-time

Pay: ₹500,000.00 - ₹1,100,000.00 per year

Benefits:

• Health insurance
• Provident Fund

Location Type:

• In-person

Schedule:

• Day shift

Work Location: In person