Nallas Corporation - Generative AI Engineer - Python/RAG

Nallas is a software services and product company providing data, cloud, product, and quality engineering services for enterprises that need to transform with predictability.

We partner with our customers on high-priority, high-visibility projects where failure is not an option.

And we only work with customers who want a partner that is invested in the same business outcomes as they are.

There are three things that make us different : we are driven by and invested in, our customer's business outcomes, we deliver with the most stable, static, and accountable teams, and provide unparalleled transparency and predictability.

We work on recent technologies like Apache, Kubernetes, Snowflake, Delta Lake, Containerization, Multi-cloud architecture, AI, Machine learning, AR/VR, Blockchain, IoT, Test Automation, and DevOps.

We enhance the data quality of our customers, the scalability, efficiency, and security for our clients, and added to that we help them reduce expenses and enhance quality.

We at Nallas follow Agile development frameworks, like Scrum and Kanban, as they provide rapid results to our customers and are adaptable to constantly changing market conditions.

We offer an outstanding work environment that fosters and assures your personal and professional development.

If you are interested in joining the organization, you may anticipate working in a collaborative, innovative, and team-oriented atmosphere.

If you are looking to work on the latest technologies and trying to take your career to the next level Join Nallas.

Experience : 2-8 yrs.

Location : Remote.

Experience : 2+ years' experience with AI/ML (1+ years in Gen AI).

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.

Skill Sets

• Expertise in Python, Data Structures, and API CallsSolid foundation for working with generative AI models and frameworks.
• Strong Communication Skills (Documentation & Presentations)
• Ability to clearly document/present complex technical concepts for both technical and non-technical audiences.
• Effective Teamwork and Solo WorkCollaborate effectively on large projects while also possessing the ability to independently drive research and development efforts.
• Data Mining and Text ProcessingExtract valuable insights from various data sources to train and improve generative models.
• Building RAG Pipelines (Highly Desired) Demonstrated experience building retrieval-augmented generation pipelines, a key technique in generative AI.
• Machine Learning (ML), Natural Language Processing (NLP), Generative Adversarial Networks (GANs), Transformers & BERT flavors, Hugging Face Model Implementation and Fine-Tuning : .
• Solid understanding of these core concepts for generative AI development.
• 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.
• Collaboration Ability to collaborate effectively with Business Analysts (BAs), Development Teams, and DevOps teams to bring generative AI solutions to life.
• 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.
• Experience with POC Tools (Streamlit, Gradio) Ability to rapidly prototype and showcase generative AI concepts.
• 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.
• 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.
  
  

(ref:hirist.tech)