Website PNF Consulting
Job Requirement
P&F Solutions is seeking an AI Engineer with experience in Agentic AI, including hands-on experience with LLM development, intelligent agent creation, and cloud-based ML platforms such as AWS SageMaker, AWS Bedrock, Azure Machine Learning, and Google Vertex AI.
This individual will play a key role in designing, building, and deploying production-grade AI solutions, helping to operationalize advanced language models that power automation, intelligent agents, and personalized user experiences across industries.
Key Skills & Experience
Natural Language Processing & Generative AI
- 2+ years of professional experience developing AI/ML models, with a strong focus on natural language understanding, text generation, semantic search, and conversation modeling.
- Deep understanding of transformer-based architectures (e.g., BERT, GPT, T5, LLaMA, Claude, Gemini).
- Hands-on experience with:
- Fine-tuning and instruction tuning of LLMs on domain-specific data.
- Embedding models and vector databases for semantic retrieval and RAG pipelines.
- Prompt engineering, zero-shot, few-shot, and chain-of-thought prompting techniques.
- Experience designing and implementing LLM-powered agents capable of multi-step reasoning, tool invocation, and external API interactions.
- Familiar with agent frameworks and orchestration tools like:
- LangChain, Haystack, Semantic Kernel, or Autogen
- Function-calling APIs (e.g., OpenAI tools, Bedrock Agents)
- Tool use, memory management, and dynamic context planning
- Built or contributed to chatbots, workflow assistants, or domain-specific copilots using both open-source and commercial models.
- Strong experience deploying NLP and GenAI workloads on:
- AWS SageMaker – model training, tuning (Hyperparameter/AutoML), multi-model endpoints, and Pipelines.
- AWS Bedrock – orchestration of foundation models (e.g., Anthropic, Cohere) into business workflows.
- Azure Machine Learning – endpoint management, AutoML, and integration with Azure OpenAI.
- Google Vertex AI – training/evaluation pipelines, model registry, and PaLM/Gemini model use.
- Skilled at deploying models as APIs using FastAPI, Flask, or managed endpoints.
- Implemented CI/CD pipelines for ML using SageMaker Pipelines, Vertex Pipelines, or GitHub Actions.
- Experienced in the full ML lifecycle: from data acquisition and preprocessing to monitoring and feedback loops.
- Proficient in:
- Model tracking and versioning (e.g., MLflow, DVC)
- Drift detection, model monitoring, and performance logging
- Data pipelines with tools like Airflow, AWS Step Functions, Glue, or BigQuery
- Aware of best practices in responsible AI, including bias detection, fairness auditing, explainability (SHAP, LIME), and GDPR/AI Act compliance.
- Proven success working cross-functionally with data scientists, software engineers, DevOps, product managers, and business stakeholders.
- Ability to turn ambiguous business problems into deployable AI solutions, with measurable KPIs.
- Strong written and verbal communication skills — capable of presenting complex AI concepts in simple terms for executives or non-technical teams.
Preferred Certifications
- AWS Certified Machine Learning – Specialty
- Azure AI Engineer Associate
- Google Professional Machine Learning Engineer
Bonus Experience
- Working knowledge of:
- Enterprise LLM deployments using guardrails, moderation APIs, and fallback strategies
- NLP/LLM Certifications
- TensorFlow Developer or PyTorch Specialist
If you are interested in this position, please send your resume to careers@pnf-consulting.com. Please include “AI Engineer” in the subject line. We look forward to hearing from you!
To apply for this job email your details to careers@pnf-consulting.com