Artificial Intelligence Engineer

Role Overview:

An

AI Engineer

is responsible for designing, building, deploying, and optimizing

AI, Machine Learning, and Generative AI solutions

that solve real business problems. This role bridges

data, models, and applications

, ensuring AI solutions are scalable, reliable, and production‑ready.

AI Engineers work closely with

product owners, data engineers, software engineers, and client stakeholders

to translate requirements into intelligent systems.

Key Responsibilities

1\. AI \& Generative AI Development

• Design and build

AI and Generative AI solutions

using

LLMs, NLP, and deep learning models

• Develop applications using

OpenAI APIs, Azure OpenAI, HuggingFace, LangChain, Amazon Bedrock

, and similar platforms [Mphasis-Na...I Engineer \| Word]

• Implement

Retrieval Augmented Generation (RAG)

pipelines using vector databases such as

FAISS and Pinecone

• Fine‑tune models using techniques like

LoRA and QLoRA

• Build AI‑powered features such as:
• Chatbots and virtual assistants
• Text summarization and extraction
• Question‑answering systems
• Speech‑to‑Text and Text‑to‑Speech solutions

2\. Machine Learning \& Deep Learning

• Build and deploy

ML models

using:

• Supervised and unsupervised learning
• Regression and classification algorithms
• Neural networks and ensemble techniques
• Develop deep learning models using

TensorFlow, PyTorch, CNNs, RNNs, LSTMs, GANs, BERT

and transformer architectures [Mphasis-Na...I Engineer \| Word]

• Evaluate model performance using metrics such as

Perplexity, BLEU, and ROUGE

3\. Prompt Engineering

• Design and optimize prompts for:
• Text summarization
• Information extraction
• Question \& Answer systems
• Apply advanced prompting techniques such as:
• Few‑shot prompting
• Chain‑of‑Thought (CoT)
• Knowledge‑base grounded prompts [Mphasis-Na...I Engineer \| Word]

4\. Data \& Backend Integration

• Work with relational and NoSQL databases:
• MS SQL Server, MySQL, PostgreSQL, MongoDB, Cassandra, HBase
• Build AI services and APIs using

Python‑based frameworks

• Integrate AI models with enterprise applications and workflows
• Ensure data quality, security, and compliance in AI pipelines

5\. Production \& Cloud Readiness

• Deploy AI solutions on cloud platforms (Azure / AWS preferred)
• Implement scalable and secure AI architectures
• Monitor, optimize, and retrain models as required
• Use AI‑assisted development tools such as

Microsoft Copilot

to accelerate development responsibly [Mphasis-Na...I Engineer \| Word]

Required Technical Skills

Programming \& Frameworks

• Strong proficiency in

Python

• NumPy, Pandas, Scikit‑learn, TensorFlow, PyTorch, spaCy, NLTK
• Experience building production‑grade AI pipelines

AI / ML / GenAI

• LLMs and Generative AI
• NLP techniques
• RAG architectures
• Embeddings (Word2Vec, GloVe, ELMo)
• Vector databases

Cloud \& Tools

• Azure OpenAI / AWS Bedrock
• HuggingFace ecosystem
• LangChain
• Model fine‑tuning and evaluation tools

Nice‑to‑Have Skills

• Experience with

enterprise AI platforms

• Knowledge of

MLOps pipelines

• Understanding of AI governance, ethics, and security
• Prior experience in

financial services or enterprise domains

Soft Skills \& Expectations

• Strong problem‑solving and analytical thinking
• Ability to translate business problems into AI solutions
• Excellent communication with technical and non‑technical stakeholders
• Fast learner with a mindset to adapt to evolving AI technologies

Typical Experience Range

• 3–6 years

for mid‑level AI Engineer

• 7\+ years

for senior / lead AI Engineer roles

• (with hands‑on AI/ML and GenAI experience)