Generative AI Engineer
2 weeks ago
We are looking for Generative AI Engineer
Exp : 5+ Years, Location: Remote.
Experience: 5+ years' experience with AI/ML (2+ years in Gen AI)
Qualifications :
- Bachelor's degree in Computer Science, Engineering, Mathematics, or related field; Master's or Ph.D.
- Proficiency in programming languages such as Python (preferred) or R,
- Strong understanding of neural networks, deep learning frameworks (TensorFlow, PyTorch), and other AI techniques.
- Excellent problem-solving skills and ability to think critically about complex technical challenges.
- Should have experience in accessing Hugging Face, TensorFlow Hub, Kaggle sites
- Experience with natural language processing (NLP), computer vision, or reinforcement learning is a plus.
- Proven track record of delivering successful AI projects from conception to deployment.
Summary :
We are seeking a skilled General AI Engineer with at least 5 years of experience to join our dynamic team. The ideal candidate will have a strong background in artificial intelligence, machine learning, and deep learning techniques.
As a General AI Engineer, you will play a crucial role in developing, implementing, and maintaining advanced AI systems that drive innovation and solve complex problems. You will collaborate with cross-functional teams to design and deploy AI solutions that enhance products and services, leveraging your expertise to push the boundaries of what AI can achieve.
Responsibilities :
1. Develop and implement cutting-edge AI algorithms and models to solve complex problems.
2. Design, build, and deploy scalable AI systems that deliver value to the organization.
3. Collaborate with cross-functional teams to gather requirements and understand business needs.
4. Conduct research to stay up-to-date with the latest advancements in AI and machine learning.
5. Optimize AI models for performance, scalability, and efficiency.
6. Provide technical guidance and mentorship to junior team members.
7. Work closely with product managers and stakeholders to define project goals and milestones.
8. Evaluate and select appropriate tools and technologies for AI development and deployment.
9. Participate in code reviews, testing, and debugging to ensure high-quality AI solutions.
10.Stay informed about industry best practices and emerging trends in AI and machine learning.
11.Communicate complex technical concepts to non-technical stakeholders.
Must Have Skill sets :
1. Expertise in Python, Data Structures, and API Calls- Solid foundation for working with generative AI models and frameworks.
2. Strong Communication Skills (Documentation & Presentations)- Ability to clearly document/present complex technical concepts for both technical and non-technical audiences.
3. Effective Teamwork and Solo Work- Collaborate effectively on large projects while also possessing the ability to independently drive research and development efforts.
4. Data Mining and Text Processing- Extract valuable insights from various data sources to train and improve generative models.
5. Building RAG Pipelines (Highly Desired) - Demonstrated experience building retrieval-augmented generation pipelines, a key technique in generative AI.
6. Machine Learning (ML), Natural Language Processing (NLP), Generative Adversarial Networks (GANs), Transformers & BERT flavors, Hugging Face Model Implementation and Fine-Tuning:
7. Solid understanding of these core concepts for generative AI development.
8. Hands-on Experience with Vector Databases: Experience with Chroma DB, PineCone, Milvus, FAISS, Arango DB for data storage and retrieval relevant to generative AI projects.
9. Collaboration- Ability to collaborate effectively with Business Analysts (BAs), Development Teams, and DevOps teams to bring generative AI solutions to life.
10. Hands-on Experience of Embedded Models: Familiarity with deploying generative models on resource-constrained devices can be a significant asset. Ex : Open AI - Ada Embedding 002 model
11. Experience with POC Tools (Streamlit, Gradio) - Ability to rapidly prototype and showcase generative AI concepts.
12. Cloud Experience (AWS Bedrock or similar)-Expertise in managing and deploying large-scale generative AI models on cloud platforms with basic knowledge such as EC2, ECS, ECR, S3, Sagemaker etc.
13. Expertise in Specific LLMs (Any one deep knowledge in OpenAI, Jurassic-1 Jumbo, LLAMA, mistral, Mixtral, Gemma, Gemini Pro) - In-depth knowledge of a particular LLM may be required depending on the specific project focus.
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