with 5-8 years of experience in building and supporting AI-driven solutions. The ideal candidate should have worked on multiple
AI-embedded products
, contributed to
Generative AI (GenAI) projects
leveraging
Retrieval-Augmented Generation (RAG)
, and established scalable
MLOps practices
. You will play a key role in designing, developing, and deploying AI/ML models, ensuring seamless integration into production environments, and driving innovation in next-gen AI applications.
Key Responsibilities
AI/GenAI Development:
Design and develop AI/ML models, with hands-on expertise in
LLMs (Large Language Models)
and
GenAI solutions
.
Implement
RAG pipelines
to improve contextual responses and accuracy of LLM-based applications.
Support and enhance multiple AI-embedded products from development to production.
Data & Pipelines:
Build and maintain
scalable data pipelines
for AI/ML training and inference.
Work with structured and unstructured data, including text, images, and multimodal inputs.
Ensure data quality, preprocessing, and feature engineering pipelines.
MLOps & Deployment:
Define and establish
MLOps best practices
for continuous integration, model monitoring, versioning, and governance.
Automate model deployment workflows and optimize inference performance.
Collaborate with DevOps/DataOps teams to integrate AI solutions with enterprise systems.
Collaboration & Innovation:
Partner with cross-functional teams (data engineers, product managers, software engineers) to deliver end-to-end AI solutions.
Stay updated with advancements in AI/GenAI, and proactively recommend innovative approaches.
Mentor junior team members and contribute to AI knowledge-sharing initiatives.
Required Skills & Experience
Core AI/ML Expertise:
5-8 years of hands-on experience in
Machine Learning, Deep Learning, and AI product development
.
Strong understanding of
LLMs (GPT, LLaMA, Falcon, etc.)
, embeddings, vector databases, and
RAG frameworks
.
Experience in
Generative AI solutions
across text, conversational AI, or multimodal domains.
Data Engineering:
Proficiency in
Python, PyTorch, TensorFlow, Hugging Face Transformers
.
Experience with
data pipelines
(Airflow, Prefect, Kafka, Spark, or similar).
Hands-on experience with
SQL/NoSQL databases
and
vector databases
(e.g., Pinecone, Weaviate, FAISS).
MLOps Practices:
Strong experience in establishing
MLOps pipelines
(CI/CD for ML, MLFlow, Kubeflow, SageMaker, Vertex AI, or similar).
Monitoring, logging, and retraining AI/ML models in production.
Containerization & orchestration (Docker, Kubernetes).
Cloud & Tools:
Expertise with at least one major
cloud platform
(AWS, Azure, GCP).
Familiarity with
API development
(REST, GraphQL) and
microservices integration
.
Soft Skills:
Strong problem-solving and analytical skills.
Ability to work in agile environments, with excellent collaboration and communication skills.
Passion for innovation and driving AI adoption in real-world products.
Preferred Qualifications