AmbitionBox
Search
Jobs
- Reviews
- Salaries
- Interview Questions
- About Company
- Benefits
- Jobs
- Office Photos
- Community
Add office photos
All Filters
149 Tech Stalwart Solution Jobs
AI Engineer
6-8 years
Kolkata, Mumbai, New Delhi + 4 more
1 vacancy
AI Engineer
Tech Stalwart Solution
posted 8hr ago
Job Role Insights
Key skills for the job
Job Description
LLM & NLP Model Development: Develop and fine-tune large language models (GPT-3/4, T5, BERT, etc.) for tasks like text generation, summarization, sentiment analysis, and information retrieval.
Agentic System Design: Build and deploy autonomous, goal-oriented agentic systems capable of interacting with complex environments to achieve specific tasks.
Project Ownership: Manage AI initiatives from ideation through to production deployment, independently handling all aspects of model lifecycle management.
Cross-Functional Collaboration: Partner with teams across product, engineering, and operations to translate business requirements into actionable AI projects, particularly in areas that leverage LLMs and agentic AI.
Model Optimization & Evaluation: Optimize models for scalability, accuracy, and efficiency; perform regular testing, monitoring, and tuning to maintain performance in production.
Research & Experimentation: Stay abreast of the latest AI advancements, including prompt engineering, reinforcement learning, and advancements in agent-based architectures, and evaluate their applicability.
Documentation & Knowledge Sharing: Document architectures, model performance, and lessons learned, while actively sharing insights and best practices across the organization.
Requirements:
Experience: 6+ years in AI and machine learning, with hands-on expertise in LLMs and agentic system development and deployment.
Proficiency in NLP & LLMs: Strong experience with large language models (GPT-3/4, Claude etc.) and understanding of transformer architectures, pretraining/fine-tuning, prompt engineering, and task adaptation.
Agentic Systems Expertise: Knowledge in creating and deploying agentic systems with a goal-oriented design, leveraging reinforcement learning (RL) or other methods for autonomous task management.
Technical Skills: Advanced proficiency in Python, ML libraries (Hugging Face Transformers, TensorFlow, PyTorch), and data processing libraries (Pandas, NumPy).
MLOps & Deployment: Experience with MLOps practices, particularly for deploying large models and agentic systems in production environments (Docker, Kubernetes, MLFlow, or similar).
Data Management: Familiarity with data engineering principles, including handling large datasets and real-time processing for model training, and experience with cloud platforms (AWS, GCP, Azure).
Independent Problem Solving: Strong analytical skills with a proven track record of working independently to solve complex AI challenges.
Communication Skills: Ability to clearly articulate technical concepts and AI-driven insights to non-technical stakeholders and document work for continuity.
Agentic System Design: Build and deploy autonomous, goal-oriented agentic systems capable of interacting with complex environments to achieve specific tasks.
Project Ownership: Manage AI initiatives from ideation through to production deployment, independently handling all aspects of model lifecycle management.
Cross-Functional Collaboration: Partner with teams across product, engineering, and operations to translate business requirements into actionable AI projects, particularly in areas that leverage LLMs and agentic AI.
Model Optimization & Evaluation: Optimize models for scalability, accuracy, and efficiency; perform regular testing, monitoring, and tuning to maintain performance in production.
Research & Experimentation: Stay abreast of the latest AI advancements, including prompt engineering, reinforcement learning, and advancements in agent-based architectures, and evaluate their applicability.
Documentation & Knowledge Sharing: Document architectures, model performance, and lessons learned, while actively sharing insights and best practices across the organization.
Requirements:
Experience: 6+ years in AI and machine learning, with hands-on expertise in LLMs and agentic system development and deployment.
Proficiency in NLP & LLMs: Strong experience with large language models (GPT-3/4, Claude etc.) and understanding of transformer architectures, pretraining/fine-tuning, prompt engineering, and task adaptation.
Agentic Systems Expertise: Knowledge in creating and deploying agentic systems with a goal-oriented design, leveraging reinforcement learning (RL) or other methods for autonomous task management.
Technical Skills: Advanced proficiency in Python, ML libraries (Hugging Face Transformers, TensorFlow, PyTorch), and data processing libraries (Pandas, NumPy).
MLOps & Deployment: Experience with MLOps practices, particularly for deploying large models and agentic systems in production environments (Docker, Kubernetes, MLFlow, or similar).
Data Management: Familiarity with data engineering principles, including handling large datasets and real-time processing for model training, and experience with cloud platforms (AWS, GCP, Azure).
Independent Problem Solving: Strong analytical skills with a proven track record of working independently to solve complex AI challenges.
Communication Skills: Ability to clearly articulate technical concepts and AI-driven insights to non-technical stakeholders and document work for continuity.
Employment Type: Full Time, Permanent
Read full job descriptionCompare Tech Stalwart Solution with
TCS
Compare
Accenture
Compare
Wipro
Compare
Cognizant
Compare
Capgemini
Compare
HDFC Bank
Compare
Infosys
Compare
ICICI Bank
Compare
HCLTech
Compare
Tech Mahindra
Compare
Genpact
Compare
Teleperformance
Compare
Concentrix Corporation
Compare
Axis Bank
Compare
Amazon
Compare
Jio
Compare
Reliance Retail
Compare
iEnergizer
Compare
IBM
Compare
LTIMindtree
Compare
Similar Jobs for you
Salesforce Health Cloud Developer
10-12 Yrs
Kolkata, Mumbai, New Delhi +4 more
EHSM (Product Compliance) Consultant
4-8 Yrs
Kolkata, Mumbai, New Delhi +4 more
Share an Interview