GenAI Engineer

The Digital Group Infotech

5 - 7 years

15 - 30 Lacs

pune

Posted:Just now| Platform:

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Skills Required

pytorch large language model python tensorflow transformers keras machine learning

Work Mode

Work from Office

Job Type

Full Time

Job Description

About the Role
We are seeking a highly skilled and motivated
Generative AI Engineer
with hands-on experience in building and deploying
multimodal AI solutions
(text, audio, and video) on
Azure or AWS AI platforms
. The ideal candidate will have deep knowledge of generative AI models (LLMs, diffusion models, etc.), and be capable of designing, developing, and scaling AI/ML solutions using cloud-native tools and services.
Key Responsibilities

Design, develop, and deploy
multimodal AI models
(text, audio, video) using
AWS or Azure
AI services.
Integrate
LLMs (e.g., OpenAI, Anthropic, Cohere)
with proprietary or third-party models to build custom applications.
Utilize
Azure OpenAI, AWS Bedrock, SageMaker,
or similar services for training and inference workflows.
Develop pipelines for
preprocessing, training, evaluation, and fine-tuning
of multimodal models.
Collaborate with product and engineering teams to define use cases and translate them into scalable GenAI solutions.
Evaluate and optimize models for
latency, accuracy, cost, and ethical considerations
.
Implement
prompt engineering, RAG (retrieval-augmented generation), vector search
, and other advanced techniques.
Stay current with GenAI research trends and contribute to internal knowledge sharing.
Required Skills & Qualifications
5+ years of experience in
AI/ML
, with 12 years specifically in
generative AI / multimodal AI
.
Strong proficiency with at least one cloud platform:
Azure AI (Azure OpenAI, Cognitive Services, ML Studio)
or
AWS AI (Bedrock, SageMaker, Transcribe, Rekognition, Polly, etc.)
.
Hands-on experience with
multimodal data processing
(e.g., NLP, audio signal processing, video analysis).
Experience with
LLMs
,
transformer architectures
, and model fine-tuning (e.g., LoRA, PEFT, adapters).
Proficiency in
Python
, and libraries such as
PyTorch, TensorFlow, Transformers (Hugging Face)
.
Experience with
vector databases
(e.g., FAISS, Pinecone, Weaviate) and
embedding generation
.
Strong understanding of
MLOps
,
CI/CD for ML
, and deployment strategies for GenAI models.

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