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GenAI Solution Architect

2 weeks ago

Greater Bengaluru Area, India Quikr Full time US$ 1,50,000 - US$ 2,00,000 per year

Job Role—GEN-AI Solution Architect

Job-Location
:
Bangalore/Hyderabad location

Experience required: 7+ years

Client Organization: OpZen.AI

Job-Requirements:

Minimum 7+ years' experience

Responsibilities/ Accountabilities of the job:

You will lead and collaborate with a team of AI/ML Developers, data scientists,Data Analysts, and other software developers to build, deploy, and maintain robust GEN AI & ML/AI pipelines for a wide range of applications from natural language processing (NLP) and computer vision (CV), to foundational model development in single-cell RNA-seq and spatial transcriptomics. Your work will ensure our models are scalable, reliable, and deliver meaningful impact to our customers and scientific community.

Key Responsibilities:


• Design and develop end-to-end Generative AI solutions using LLMs (Large Language Models), NLP, and related AI/ML technologies

Key Responsibilities:

Design and develop end-to-end Generative AI solutions using LLMs (Large Language Models), NLP, and related AI/ML technologies.

Architect scalable AI systems that integrate with enterprise products or client services.

Write high-quality, production-ready code using Python or other relevant languages/frameworks.

Collaborate with product managers, data scientists, and engineering teams to implement and optimize GenAI use cases.

Evaluate and select appropriate LLM frameworks (OpenAI, Azure OpenAI, Hugging Face, Langchain, etc.).

Drive architecture decisions, ensuring performance, scalability, and security.

Lead, mentor, and manage AI/ML developers and cross-functional technical teams.

Own project delivery from design to deployment with strong documentation and communication.

Stay updated on emerging trends and best practices in the GenAI ecosystem.

Design and build ML models (including classical ML and deep learning models) based on structured and unstructured data


•


• Build robust APIs and micro services to serve models to internal and external users.


• Optimize model performance and infrastructure (latency, memory, and throughput) to ensure scalability and cost-efficiency.


• Monitor models in production and implement continuous learning, versioning, and rollback strategies.


• Collaborate with data scientists, engineers and product teams to translate scientific problems into ML solutions.


• Provide technical mentorship and promote best practices in ML engineering and MLOps.

Required Skills & Experience:


• Proficient in Python and modern development practices (Git, CI/CD, Agile)


• Hands-on with ML frameworks: TensorFlow, PyTorch, Scikit-learn


• Strong grasp of statistical learning and ML fundamentals


• Familiar with MLOps tools: MLflow OR Airflow


• Skilled in cloud platforms (AWS, GCP, Azure) and resource optimization


• Knowledge of hardware acceleration (GPU, TPU) and model optimization


• Exposure to agentic AI concepts and systems, including tool-use, autonomous decision-making, and orchestration of multi-step tasks.


• Strong communication and collaboration skills to work effectively with cross-functional stakeholders


• Proven experience in deploying ML models to production environments.

Nice-to-have:


• Background in biology, genomics, or bioinformatics (to contextualize biomedical data).


• Experience with model optimization (quantization, pruning) and open-source contributions (ML/bioinformatics tools).


• Knowledge of software development best practices (Agile, APIs, and micro services).


• Expertise in low-latency, high-throughput model deployment (REST, gRPC, streaming)