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Engaged Employer

TechVantage Systems

Compare

2.7

based on 20 Reviews

12 TechVantage Systems Jobs

Gen AI Architect

TechVantage Systems Private Limited

2.7

based on 20 Reviews

5-10 years

Thiruvananthapuram

1 vacancy

Gen AI Architect

TechVantage Systems

posted 1mon ago

Job Role Insights

Flexible timing

Key skills for the job

Data Science Computer Science Data Analytics Python Architecting Artificial Intelligence

+ 4 more

Job Description

Techvantage Analytics is a fast-growing AI services and product engineering company specializing in Analytics, Machine Learning, and AI-based solutions. We are seeking a forward-thinking and highly experienced Gen AI Architect to design and implement cutting-edge AI solutions. The role involves leveraging the latest advancements in artificial intelligence and data science, including foundation models, multimodal AI, federated learning, and emerging AI frameworks. The ideal candidate will drive the development and deployment of scalable AI systems that power innovative applications across diverse domains.

What we are looking from an ideal candidate

AI Architecture Design: Develop robust architectures for generative AI solutions, leveraging the latest advancements in foundation models, large language models (LLMs), and multimodal AI.
Model Development: Build and fine-tune advanced generative models, such as GPT-based models, diffusion models, and transformers, for applications in text, image, video, and audio generation.
Framework Integration: Integrate modern AI frameworks, such as LangChain and LlamaIndex, to enhance the capabilities of generative models in production environments.
Data Strategy: Implement data strategies including synthetic data generation, federated learning, and secure data sharing protocols for effective model training.
Advanced Deployment: Design and oversee the deployment of scalable AI systems using the latest technologies, such as MLOps pipelines, container orchestration (Docker, Kubernetes), and serverless architectures.
Ethics Compliance: Ensure AI models comply with ethical guidelines, including responsible AI principles, addressing fairness, transparency, and mitigating bias.
Research Innovation: Stay updated with the latest AI trends, such as reinforcement learning with human feedback (RLHF), AI explainability, and continual learning, and apply them to real-world problems.
Collaboration: Work closely with cross-functional teams, including data engineers, scientists, and product managers, to align AI systems with business goals.
Leadership Mentorship: Provide guidance to junior AI practitioners and foster a culture of innovation within the team.
Performance Monitoring: Define KPIs for model performance, monitor systems in production, and implement mechanisms for continuous improvement and retraining.

Preferred Skills:
What skills do you need

Bachelor s, Master s, or Ph.D. in Computer Science, Artificial Intelligence, Data Science, or related fields.
5+ years of experience in AI/ML, with a focus on generative AI technologies.
Proven expertise in deploying large-scale generative models (e.g., GPT-4, Stable Diffusion, DALL-E) in production environments.
Hands-on experience with AI/ML frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers.
Strong knowledge of LLM fine-tuning, zero-shot learning, and multimodal AI.
Proficiency in Python, R, or Julia for data science and AI model development.
Expertise in cloud-native AI solutions using AWS SageMaker, Google Vertex AI, or Azure Machine Learning.
Familiarity with advanced data strategies like synthetic data creation, federated learning, and privacy-preserving AI.
Hands-on experience with MLOps practices, including monitoring tools like MLflow and Kubeflow.
Knowledge of real-time AI applications using edge computing and IoT.
Ability to conceptualize innovative solutions using the latest AI technologies.
Advanced analytical skills to troubleshoot complex AI challenges.
Ability to thrive in a dynamic, fast-paced environment with emerging technologies.
Proven track record of working effectively within cross-functional and geographically distributed teams.
Commitment to developing high-quality, reliable AI systems.